Graham Neubig


2024

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Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models
Seungone Kim | Juyoung Suk | Shayne Longpre | Bill Yuchen Lin | Jamin Shin | Sean Welleck | Graham Neubig | Moontae Lee | Kyungjae Lee | Minjoon Seo
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Proprietary LMs such as GPT-4 are often employed to assess the quality of responses from various LMs. However, concerns including transparency, controllability, and affordability strongly motivate the development of open-source LMs specialized in evaluations. On the other hand, existing open evaluator LMs exhibit critical shortcomings: 1) they issue scores that significantly diverge from those assigned by humans, and 2) they lack the flexibility to perform both direct assessment and pairwise ranking, the two most prevalent forms of assessment. Additionally, they do not possess the ability to evaluate based on custom evaluation criteria, focusing instead on general attributes like helpfulness and harmlessness. To address these issues, we introduce Prometheus 2, a more powerful evaluator LM than its predecessor that closely mirrors human and GPT-4 judgements. Moreover, it is capable of processing both direct assessment and pair-wise ranking formats grouped with a user-defined evaluation criteria. On four direct assessment benchmarks and four pairwise ranking benchmarks, Prometheus 2 scores the highest correlation and agreement with humans and proprietary LM judges among all tested open evaluator LMs. Our models, code, and data are all publicly available.

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VIMI: Grounding Video Generation through Multi-modal Instruction
Yuwei Fang | Willi Menapace | Aliaksandr Siarohin | Tsai-Shien Chen | Kuan-Chieh Wang | Ivan Skorokhodov | Graham Neubig | Sergey Tulyakov
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Existing text-to-video diffusion models rely solely on text-only encoders for their pretraining. This limitation stems from the absence of large-scale multimodal prompt video datasets, resulting in a lack of visual grounding and restricting their versatility and application in multimodal integration. To address this, we construct a large-scale multimodal prompt dataset by employing retrieval methods to pair in-context examples with the given text prompts and then utilize a two-stage training strategy to enable diverse video generation tasks within a model. In the first stage, we propose a multimodal conditional video generation framework for pretraining on these augmented datasets, establishing a foundational model for grounded video generation. Secondly, we fine-tune the model from the first stage on various video generation tasks, incorporating multimodal instructions. This process further refines the model’s ability to handle diverse inputs and tasks, ensuring seamless integration of multimodal information. After this two-stage training process, VIMI demonstrates multimodal understanding capabilities, producing contextually rich and personalized videos grounded in the provided inputs, as shown in Figure1. Compared to previous subject-driven video generation methods, our generator can synthesize consistent and temporally coherent videos with large motion while retaining the semantic control. Our generator also achieves state-of-the-art text-to-video generation results on UCF101 benchmark.

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An image speaks a thousand words, but can everyone listen? On image transcreation for cultural relevance
Simran Khanuja | Sathyanarayanan Ramamoorthy | Yueqi Song | Graham Neubig
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Given the rise of multimedia content, human translators increasingly focus on culturally adapting not only words but also other modalities such as images to convey the same meaning. While several applications stand to benefit from this, machine translation systems remain confined to dealing with language in speech and text. In this work, we introduce a new task of translating images to make them culturally relevant. First, we build three pipelines comprising state-of-the-art generative models to do the task. Next, we build a two-part evaluation dataset – (i) concept: comprising 600 images that are cross-culturally coherent, focusing on a single concept per image; and (ii) application: comprising 100 images curated from real-world applications. We conduct a multi-faceted human evaluation of translated images to assess for cultural relevance and meaning preservation. We find that as of today, image-editing models fail at this task, but can be improved by leveraging LLMs and retrievers in the loop. Best pipelines can only translate 5% of images for some countries in the easier concept dataset and no translation is successful for some countries in the application dataset, highlighting the challenging nature of the task. Our project webpage is here: https://machine-transcreation.github.io/image-transcreation and our code, data and model outputs can be found here: https://github.com/simran-khanuja/image-transcreation.

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GlossLM: A Massively Multilingual Corpus and Pretrained Model for Interlinear Glossed Text
Michael Ginn | Lindia Tjuatja | Taiqi He | Enora Rice | Graham Neubig | Alexis Palmer | Lori Levin
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Language documentation projects often involve the creation of annotated text in a format such as interlinear glossed text (IGT), which captures fine-grained morphosyntactic analyses in a morpheme-by-morpheme format. However, there are few existing resources providing large amounts of standardized, easily accessible IGT data, limiting their applicability to linguistic research, and making it difficult to use such data in NLP modeling. We compile the largest existing corpus of IGT data from a variety of sources, covering over 450k examples across 1.8k languages, to enable research on crosslingual transfer and IGT generation. We normalize much of our data to follow a standard set of labels across languages.Furthermore, we explore the task of automatically generating IGT in order to aid documentation projects. As many languages lack sufficient monolingual data, we pretrain a large multilingual model on our corpus. We demonstrate the utility of this model by finetuning it on monolingual corpora, outperforming SOTA models by up to 6.6%. Our pretrained model and dataset are available on Hugging Face: https://huggingface.co/collections/lecslab/glosslm-66da150854209e910113dd87

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Large Language Models Enable Few-Shot Clustering
Vijay Viswanathan | Kiril Gashteovski | Kiril Gashteovski | Carolin Lawrence | Tongshuang Wu | Graham Neubig
Transactions of the Association for Computational Linguistics, Volume 12

Unlike traditional unsupervised clustering, semi-supervised clustering allows users to provide meaningful structure to the data, which helps the clustering algorithm to match the user’s intent. Existing approaches to semi-supervised clustering require a significant amount of feedback from an expert to improve the clusters. In this paper, we ask whether a large language model (LLM) can amplify an expert’s guidance to enable query-efficient, few-shot semi-supervised text clustering. We show that LLMs are surprisingly effective at improving clustering. We explore three stages where LLMs can be incorporated into clustering: before clustering (improving input features), during clustering (by providing constraints to the clusterer), and after clustering (using LLMs post-correction). We find that incorporating LLMs in the first two stages routinely provides significant improvements in cluster quality, and that LLMs enable a user to make trade-offs between cost and accuracy to produce desired clusters. We release our code and LLM prompts for the public to use.1

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Do LLMs Exhibit Human-like Response Biases? A Case Study in Survey Design
Lindia Tjuatja | Valerie Chen | Tongshuang Wu | Ameet Talwalkwar | Graham Neubig
Transactions of the Association for Computational Linguistics, Volume 12

One widely cited barrier to the adoption of LLMs as proxies for humans in subjective tasks is their sensitivity to prompt wording—but interestingly, humans also display sensitivities to instruction changes in the form of response biases. We investigate the extent to which LLMs reflect human response biases, if at all. We look to survey design, where human response biases caused by changes in the wordings of “prompts” have been extensively explored in social psychology literature. Drawing from these works, we design a dataset and framework to evaluate whether LLMs exhibit human-like response biases in survey questionnaires. Our comprehensive evaluation of nine models shows that popular open and commercial LLMs generally fail to reflect human-like behavior, particularly in models that have undergone RLHF. Furthermore, even if a model shows a significant change in the same direction as humans, we find that they are sensitive to perturbations that do not elicit significant changes in humans. These results highlight the pitfalls of using LLMs as human proxies, and underscore the need for finer-grained characterizations of model behavior.1

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Solving NLP Problems through Human-System Collaboration: A Discussion-based Approach
Masahiro Kaneko | Graham Neubig | Naoaki Okazaki
Findings of the Association for Computational Linguistics: EACL 2024

Humans work together to solve common problems by having discussions, explaining, and agreeing or disagreeing with each other.Similarly, if a system can have discussions with human partners when solving tasks, it has the potential to improve the system’s performance and reliability.In previous research on explainability, it has only been possible for systems to make predictions and for humans to ask questions about them, rather than having a mutual exchange of opinions.This research aims to create a dataset and computational framework for systems that discuss and refine their predictions through dialogue. Through experiments, we show that the proposed system can have beneficial discussions with humans, improving the accuracy by up to 25 points on a natural language inference task.

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Better Synthetic Data by Retrieving and Transforming Existing Datasets
Saumya Gandhi | Ritu Gala | Vijay Viswanathan | Tongshuang Wu | Graham Neubig
Findings of the Association for Computational Linguistics: ACL 2024

Despite recent advances in large language models, building dependable and deployable NLP models typically requires abundant, high-quality training data. However, task-specific data is not available for many use cases, and manually curating task-specific data is labor-intensive. Recent work has studied prompt-driven synthetic data generation using large language models, but these generated datasets tend to lack complexity and diversity. To address these limitations, we introduce a method, _DataTune_, to make better use of existing, publicly available datasets to improve automatic dataset generation. DataTune performs _dataset transformation_, enabling the repurposing of publicly available datasets into a format that is directly aligned with the specific requirements of target tasks. On a diverse set of language-based tasks from the BIG-Bench benchmark, we find that finetuning language models via DataTune improves over a few-shot prompting baseline by 49% and improves over existing methods that use synthetic or retrieved training data by 34%. We find that dataset transformation significantly increases the diversity and difficulty of generated data on many tasks. We release a Python package and open-source repository to make this method accessible to the community (URL will be added upon acceptance).

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Synthetic Multimodal Question Generation
Ian Wu | Sravan Jayanthi | Vijay Viswanathan | Simon Rosenberg | Sina Khoshfetrat Pakazad | Tongshuang Wu | Graham Neubig
Findings of the Association for Computational Linguistics: EMNLP 2024

Multimodal Retrieval Augmented Generation (MMRAG) is a powerful approach to question-answering over multimodal documents. A key challenge with evaluating MMRAG is the paucity of high-quality datasets matching the question styles and modalities of interest. In light of this, we propose SMMQG, a synthetic data generation framework. SMMQG leverages interplay between a retriever, large language model (LLM) and large multimodal model (LMM) to generate question and answer pairs directly from multimodal documents, with the questions conforming to specified styles and modalities. We use SMMQG to generate an MMRAG dataset of 1024 questions over Wikipedia documents and evaluate state-of-the-art models using it, revealing insights into model performance that are attainable only through style- and modality-specific evaluation data. Next, we measure the quality of data produced by SMMQG via a human study. We find that the quality of SMMQG-generated synthetic data is on par with the quality of the crowdsourced benchmark MMQA and that downstream evaluation results using both datasets strongly concur.

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Program-Aided Reasoners (Better) Know What They Know
Anubha Kabra | Sanketh Rangreji | Yash Mathur | Aman Madaan | Emmy Liu | Graham Neubig
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Prior work shows that program-aided reasoning, in which large language models (LLMs) are combined with programs written in programming languages such as Python, can significantly improve accuracy on various reasoning tasks. However, while accuracy is essential, it is also important for such reasoners to “know what they know”, which can be quantified through the calibration of the model. In this paper, we compare the calibration of Program Aided Language Models (PAL) and text-based Chain-of-thought (COT) prompting techniques over 5 datasets and 2 model types - LLaMA models and OpenAI models. Our results indicate that PAL leads to improved calibration in 75% of the instances. Our analysis uncovers that prompting styles that produce lesser diversity in generations also have more calibrated results, and thus we also experiment with inducing lower generation diversity using temperature scaling and find that for certain temperatures, PAL is not only more accurate but is also more calibrated than COT. Overall, we demonstrate that, in the majority of cases, program-aided reasoners better know what they know than text-based counterparts.

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DeMuX: Data-efficient Multilingual Learning
Simran Khanuja | Srinivas Gowriraj | Lucio Dery | Graham Neubig
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Pre-trained multilingual models have enabled deployment of NLP technologies for multiple languages. However, optimally fine-tuning these models under an annotation budget, such that performance on desired target languages is jointly maximized, still remains an open question. In this paper, we introduce DeMuX, a framework that prescribes the exact data-points to label from vast amounts of unlabelled multilingual data, having unknown degrees of overlap with the target set. Unlike most prior works, our end-to-end framework is language-agnostic, accounts for model representations, and supports multilingual target configurations. Our active learning strategies rely upon distance and uncertainty measures to select task-specific neighbors that are most informative to label, given a model. DeMuX outperforms strong baselines in 84% of the test cases, in the zero-shot setting of disjoint source and target language sets (including multilingual target pools), across three models and four tasks. Notably, in low-budget settings (5-100 examples), we observe gains of up to 8-11 F1 points. Our code is released here: https://github.com/simran-khanuja/demux.

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CMU’s IWSLT 2024 Simultaneous Speech Translation System
Xi Xu | Siqi Ouyang | Brian Yan | Patrick Fernandes | William Chen | Lei Li | Graham Neubig | Shinji Watanabe
Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)

This paper describes CMU’s submission to the IWSLT 2024 Simultaneous Speech Translation (SST) task for translating English speech to German text in a streaming manner. Our end-to-end speech-to-text (ST) system integrates the WavLM speech encoder, a modality adapter, and the Llama2-7B-Base model as the decoder. We employ a two-stage training approach: initially, we align the representations of speech and text, followed by full fine-tuning. Both stages are trained on MuST-c v2 data with cross-entropy loss. We adapt our offline ST model for SST using a simple fixed hold-n policy. Experiments show that our model obtains an offline BLEU score of 31.1 and a BLEU score of 29.5 under 2 seconds latency on the MuST-C-v2 tst-COMMON.

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CMU’s IWSLT 2024 Offline Speech Translation System: A Cascaded Approach For Long-Form Robustness
Brian Yan | Patrick Fernandes | Jinchuan Tian | Siqi Ouyang | William Chen | Karen Livescu | Lei Li | Graham Neubig | Shinji Watanabe
Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)

This work describes CMU’s submission to the IWSLT 2024 Offline Speech Translation (ST) Shared Task for translating English speech to German, Chinese, and Japanese text. We are the first participants to employ a long-form strategy which directly processes unsegmented recordings without the need for a separate voice-activity detection stage (VAD). We show that the Whisper automatic speech recognition (ASR) model has a hallucination problem when applied out-of-the-box to recordings containing non-speech noises, but a simple noisy fine-tuning approach can greatly enhance Whisper’s long-form robustness across multiple domains. Then, we feed English ASR outputs into fine-tuned NLLB machine translation (MT) models which are decoded using COMET-based Minimum Bayes Risk. Our VAD-free ASR+MT cascade is tested on TED talks, TV series, and workout videos and shown to outperform prior winning IWSLT submissions and large open-source models.

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Wav2Gloss: Generating Interlinear Glossed Text from Speech
Taiqi He | Kwanghee Choi | Lindia Tjuatja | Nathaniel Robinson | Jiatong Shi | Shinji Watanabe | Graham Neubig | David Mortensen | Lori Levin
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Thousands of the world’s languages are in danger of extinction—a tremendous threat to cultural identities and human language diversity. Interlinear Glossed Text (IGT) is a form of linguistic annotation that can support documentation and resource creation for these languages’ communities. IGT typically consists of (1) transcriptions, (2) morphological segmentation, (3) glosses, and (4) free translations to a majority language. We propose Wav2Gloss: a task in which these four annotation components are extracted automatically from speech, and introduce the first dataset to this end, Fieldwork: a corpus of speech with all these annotations, derived from the work of field linguists, covering 37 languages, with standard formatting, and train/dev/test splits. We provide various baselines to lay the groundwork for future research on IGT generation from speech, such as end-to-end versus cascaded, monolingual versus multilingual, and single-task versus multi-task approaches.

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VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks
Jing Yu Koh | Robert Lo | Lawrence Jang | Vikram Duvvur | Ming Lim | Po-Yu Huang | Graham Neubig | Shuyan Zhou | Russ Salakhutdinov | Daniel Fried
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Autonomous agents capable of planning, reasoning, and executing actions on the web offer a promising avenue for automating computer tasks. However, the majority of existing benchmarks primarily focus on text-based agents, neglecting many natural tasks that require visual information to effectively solve. Given that most computer interfaces cater to human perception, visual information often augments textual data in ways that text-only models struggle to harness effectively. To bridge this gap, we introduce VisualWebArena, a benchmark designed to assess the performance of multimodal web agents on *realistic visually grounded tasks*. VisualWebArena comprises of a set of diverse and complex web-based tasks that evaluate various capabilities of autonomous multimodal agents. To perform on this benchmark, agents need to accurately process image-text inputs, interpret natural language instructions, and execute actions on websites to accomplish user-defined objectives. We conduct an extensive evaluation of state-of-the-art LLM-based autonomous agents, including several multimodal models. Through extensive quantitative and qualitative analysis, we identify several limitations of text-only LLM agents, and reveal gaps in the capabilities of state-of-the-art multimodal language agents. VisualWebArena provides a framework for evaluating multimodal autonomous language agents, and offers insights towards building stronger autonomous agents for the web.

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Instruction-tuned Language Models are Better Knowledge Learners
Zhengbao Jiang | Zhiqing Sun | Weijia Shi | Pedro Rodriguez | Chunting Zhou | Graham Neubig | Xi Lin | Wen-tau Yih | Srini Iyer
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In order for large language model (LLM)-based assistants to effectively adapt to evolving information needs, it must be possible to update their factual knowledge through continued training on new data. The standard recipe for doing so involves continued pre-training on new documents followed by instruction-tuning on question-answer (QA) pairs. However, we find that LLMs trained with this recipe struggle to answer questions, even though the perplexity of documents is minimized. We found that QA pairs are generally straightforward, while documents are more complex, weaving many factual statements together in an intricate manner. Therefore, we hypothesize that it is beneficial to expose LLMs to QA pairs before continued pre-training on documents so that the process of encoding knowledge from complex documents takes into account how this knowledge is accessed through questions. Based on this, we propose pre-instruction-tuning (PIT), a method that instruction-tunes on questions prior to training on documents. This contrasts with standard instruction-tuning, which learns how to extract knowledge after training on documents. Extensive experiments and ablation studies demonstrate that pre-instruction-tuning significantly enhances the ability of LLMs to absorb knowledge from new documents, outperforming standard instruction-tuning by 17.8%.

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SOTOPIA-π: Interactive Learning of Socially Intelligent Language Agents
Ruiyi Wang | Haofei Yu | Wenxin Zhang | Zhengyang Qi | Maarten Sap | Yonatan Bisk | Graham Neubig | Hao Zhu
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Humans learn social skills through both imitation and social interaction. This social learning process is largely understudied by existing research on building language agents. Motivated by this gap, we propose an interactive learning method, SOTOPIA-π, that improves the social intelligence of language agents. This method leverages behavior cloning and self-reinforcement based training on filtered social interaction data according to large language model (LLM) rating. We show that our training method allows a 7B LLM to reach the social goal completion ability of an expert model (GPT-4-based agent) without the loss of more generic abilities, such as the ability to answer knowledge-based questions. We also demonstrate that this training paradigm uncovers some weaknesses in standard evaluation and safety training paradigms that (1) LLM-based evaluation of social intelligence overestimates the abilities of the language agents trained specifically for social interaction, and that (2) despite not training for better safety or question answering (QA) ability, our methods improve the safety of language agents and maintain general QA ability on the MMLU benchmark.

2023

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When Does Translation Require Context? A Data-driven, Multilingual Exploration
Patrick Fernandes | Kayo Yin | Emmy Liu | André Martins | Graham Neubig
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Although proper handling of discourse significantly contributes to the quality of machine translation (MT), these improvements are not adequately measured in common translation quality metrics. Recent works in context-aware MT attempt to target a small set of discourse phenomena during evaluation, however not in a fully systematic way. In this paper, we develop the Multilingual Discourse-Aware (MuDA) benchmark, a series of taggers that identify and evaluate model performance on discourse phenomena in any given dataset. The choice of phenomena is inspired by a novel methodology to systematically identify translations that require context. This methodology confirms the difficulty of previously studied phenomena while uncovering others which were not previously addressed. We find that commonly studied context-aware MT models make only marginal improvements over context-agnostic models, which suggests these models do not handle these ambiguities effectively. We release code and data for 14 language pairs to encourage the MT community to focus on accurately capturing discourse phenomena. Code available at https://github.com/neulab/contextual-mt

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DataFinder: Scientific Dataset Recommendation from Natural Language Descriptions
Vijay Viswanathan | Luyu Gao | Tongshuang Wu | Pengfei Liu | Graham Neubig
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Modern machine learning relies on datasets to develop and validate research ideas. Given the growth of publicly available data, finding the right dataset to use is increasingly difficult. Any research question imposes explicit and implicit constraints on how well a given dataset will enable researchers to answer this question, such as dataset size, modality, and domain. We operationalize the task of recommending datasets given a short natural language description of a research idea, to help people find relevant datasets for their needs. Dataset recommendation poses unique challenges as an information retrieval problem; datasets are hard to directly index for search and there are no corpora readily available for this task. To facilitate this task, we build the DataFinder Dataset which consists of a larger automatically-constructed training set (17.5K queries) and a smaller expert-annotated evaluation set (392 queries). Using this data, we compare various information retrieval algorithms on our test set and present a superior bi-encoder retriever for text-based dataset recommendation. This system, trained on the DataFinder Dataset, finds more relevant search results than existing third-party dataset search engines. To encourage progress on dataset recommendation, we release our dataset and models to the public.

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Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval
John Wieting | Jonathan Clark | William Cohen | Graham Neubig | Taylor Berg-Kirkpatrick
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Contrastive learning has been successfully used for retrieval of semantically aligned sentences, but it often requires large batch sizes or careful engineering to work well. In this paper, we instead propose a generative model for learning multilingual text embeddings which can be used to retrieve or score sentence pairs. Our model operates on parallel data in N languages and, through an approximation we introduce, efficiently encourages source separation in this multilingual setting, separating semantic information that is shared between translations from stylistic or language-specific variation. We show careful large-scale comparisons between contrastive and generation-based approaches for learning multilingual text embeddings, a comparison that has not been done to the best of our knowledge despite the popularity of these approaches. We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval - the last of which we introduce in this paper. Overall, our model outperforms both a strong contrastive and generative baseline on these tasks.

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MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages
Zhiruo Wang | Grace Cuenca | Shuyan Zhou | Frank F. Xu | Graham Neubig
Findings of the Association for Computational Linguistics: EACL 2023

While there has been a recent burgeoning of applications at the intersection of natural and programming languages, such as code generation and code summarization, these applications are usually English-centric. This creates a barrier for program developers who are not proficient in English. To mitigate this gap in technology development across languages, we propose a multilingual dataset, MCoNaLa, to benchmark code generation from natural language commands extending beyond English. Modeled off of the methodology from the English Code/Natural Language Challenge (CoNaLa) dataset, we annotated a total of 896 NL-Code pairs in three languages: Spanish, Japanese, and Russian. We present a systematic evaluation on MCoNaLa by testing state-of-the-art code generation systems. Although the difficulties vary across three languages, all systems lag significantly behind their English counterparts, revealing the challenges in adapting code generation to new languages.

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A Multi-dimensional Evaluation of Tokenizer-free Multilingual Pretrained Models
Jimin Sun | Patrick Fernandes | Xinyi Wang | Graham Neubig
Findings of the Association for Computational Linguistics: EACL 2023

Recent works on tokenizer-free multilingual pretrained models show promising results in improving cross-lingual transfer and reducing engineering overhead compared to subword-based alternatives. However, previous work mainly focuses on reporting accuracy on a limited set of tasks and data settings, placing less emphasis on other important factors when tuning and deploying the models in practice, such as memory usage, inference speed, and finetuning data efficiency. We attempt to fill this gap by performing a comprehensive empirical comparison of multilingual tokenizer-free and subword-based models considering the various dimensions. Surprisingly, we find that subword-based models might still be the most practical choice in many settings, achieving better performance for lower inference latency and memory usage. Based on these results, we encourage future work in tokenizer-free methods to consider these factors when designing and evaluating new models.

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Multi-lingual and Multi-cultural Figurative Language Understanding
Anubha Kabra | Emmy Liu | Simran Khanuja | Alham Fikri Aji | Genta Winata | Samuel Cahyawijaya | Anuoluwapo Aremu | Perez Ogayo | Graham Neubig
Findings of the Association for Computational Linguistics: ACL 2023

Figurative language permeates human communication, but at the same time is relatively understudied in NLP. Datasets have been created in English to accelerate progress towards measuring and improving figurative language processing in language models (LMs). However, the use of figurative language is an expression of our cultural and societal experiences, making it difficult for these phrases to be universally applicable. In this work, we create a figurative language inference dataset, {pasted macro ‘DATASETNAME’}, for seven diverse languages associated with a variety of cultures: Hindi, Indonesian, Javanese, Kannada, Sundanese, Swahili and Yoruba. Our dataset reveals that each language relies on cultural and regional concepts for figurative expressions, with the highest overlap between languages originating from the same region. We assess multilingual LMs’ abilities to interpret figurative language in zero-shot and few-shot settings. All languages exhibit a significant deficiency compared to English, with variations in performance reflecting the availability of pre-training and fine-tuning data, emphasizing the need for LMs to be exposed to a broader range of linguistic and cultural variation during training. Data and code is released at https://anonymous.4open.science/r/Multilingual-Fig-QA-7B03/

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Multi-Dimensional Evaluation of Text Summarization with In-Context Learning
Sameer Jain | Vaishakh Keshava | Swarnashree Mysore Sathyendra | Patrick Fernandes | Pengfei Liu | Graham Neubig | Chunting Zhou
Findings of the Association for Computational Linguistics: ACL 2023

Evaluation of natural language generation (NLG) is complex and multi-dimensional. Generated text can be evaluated for fluency, coherence, factuality, or any other dimensions of interest. Most frameworks that perform such multi-dimensional evaluation require training on large manually or synthetically generated datasets. In this paper, we study the efficacy of large language models as multi-dimensional evaluators using in-context learning, obviating the need for large training datasets. Our experiments show that in-context learning-based evaluators are competitive with learned evaluation frameworks for the task of text summarization, establishing state-of-the-art on dimensions such as relevance and factual consistency. We then analyze the effects of factors such as the selection and number of in-context examples on performance. Finally, we study the efficacy of in-context learning-based evaluators in evaluating zero-shot summaries written by large language models such as GPT-3.

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NusaCrowd: Open Source Initiative for Indonesian NLP Resources
Samuel Cahyawijaya | Holy Lovenia | Alham Fikri Aji | Genta Winata | Bryan Wilie | Fajri Koto | Rahmad Mahendra | Christian Wibisono | Ade Romadhony | Karissa Vincentio | Jennifer Santoso | David Moeljadi | Cahya Wirawan | Frederikus Hudi | Muhammad Satrio Wicaksono | Ivan Parmonangan | Ika Alfina | Ilham Firdausi Putra | Samsul Rahmadani | Yulianti Oenang | Ali Septiandri | James Jaya | Kaustubh Dhole | Arie Suryani | Rifki Afina Putri | Dan Su | Keith Stevens | Made Nindyatama Nityasya | Muhammad Adilazuarda | Ryan Hadiwijaya | Ryandito Diandaru | Tiezheng Yu | Vito Ghifari | Wenliang Dai | Yan Xu | Dyah Damapuspita | Haryo Wibowo | Cuk Tho | Ichwanul Karo Karo | Tirana Fatyanosa | Ziwei Ji | Graham Neubig | Timothy Baldwin | Sebastian Ruder | Pascale Fung | Herry Sujaini | Sakriani Sakti | Ayu Purwarianti
Findings of the Association for Computational Linguistics: ACL 2023

We present NusaCrowd, a collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have brought together 137 datasets and 118 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their value is demonstrated through multiple experiments.NusaCrowd’s data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and the local languages of Indonesia. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and the local languages of Indonesia. Our work strives to advance natural language processing (NLP) research for languages that are under-represented despite being widely spoken.

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Execution-Based Evaluation for Open-Domain Code Generation
Zhiruo Wang | Shuyan Zhou | Daniel Fried | Graham Neubig
Findings of the Association for Computational Linguistics: EMNLP 2023

To extend the scope of coding queries to more realistic settings, we propose ODEX, the first Open-Domain EXecution-based natural language (NL) to Python code generation dataset. ODEX has 945 NL-Code pairs spanning 79 diverse libraries, along with 1,707 human-written test cases for execution. Our NL-Code pairs are harvested from StackOverflow forums to encourage natural and practical coding queries. Moreover, ODEX supports four natural languages as intents, in English, Spanish, Japanese, and Russian. ODEX unveils intriguing behavioral differences among top-performing code language models (LM). While CODEX achieves better overall results, CODEGEN improves effectively via scaling – CODEGEN 6.1B performs comparably with CODEX 12B. Both models show substantial gaps between open and closed domains, but CODEGEN gaps tend to decrease with model size while CODEX gaps increase. We release ODEX to facilitate research into open-domain problems for the code generation community.

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Teacher Perception of Automatically Extracted Grammar Concepts for L2 Language Learning
Aditi Chaudhary | Arun Sampath | Ashwin Sheshadri | Antonios Anastasopoulos | Graham Neubig
Findings of the Association for Computational Linguistics: EMNLP 2023

One of the challenges in language teaching is how best to organize rules regarding syntax, semantics, or phonology in a meaningful manner. This not only requires content creators to have pedagogical skills, but also have that language’s deep understanding. While comprehensive materials to develop such curricula are available in English and some broadly spoken languages, for many other languages, teachers need to manually create them in response to their students’ needs. This is challenging because i) it requires that such experts be accessible and have the necessary resources, and ii) describing all the intricacies of a language is time-consuming and prone to omission. In this work, we aim to facilitate this process by automatically discovering and visualizing grammar descriptions. We extract descriptions from a natural text corpus that answer questions about morphosyntax (learning of word order, agreement, case marking, or word formation) and semantics (learning of vocabulary). We apply this method for teaching two Indian languages, Kannada and Marathi, which, unlike English, do not have well-developed resources for second language learning. To assess the perceived utility of the extracted material, we enlist the help of language educators from schools in North America to perform a manual evaluation, who find the materials have potential to be used for their lesson preparation and learner evaluation.

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T5Score: Discriminative Fine-tuning of Generative Evaluation Metrics
Yiwei Qin | Weizhe Yuan | Graham Neubig | Pengfei Liu
Findings of the Association for Computational Linguistics: EMNLP 2023

Modern embedding-based metrics for evaluation of generated text generally fall into one of two paradigms: discriminative metrics that are trained to directly predict which outputs are of higher quality according to supervised human annotations, and generative metrics that are trained to evaluate text based on the probabilities of a generative model. Both have their advantages; discriminative metrics are able to directly optimize for the problem of distinguishing between good and bad outputs, while generative metrics can be trained using abundant raw text. In this paper, we present a framework that combines the best of both worlds, using both supervised and unsupervised signals from whatever data we have available. We operationalize this idea by training T5Score, a metric that uses these training signals with mT5 as backbone. We perform an extensive empirical comparison with other existing metrics on 5 datasets, 19 languages and 280 systems, demonstrating the utility of our method. Experimental results show that: T5Score achieves the best performance on all datasets against existing top-scoring metrics at the segment level.

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It’s MBR All the Way Down: Modern Generation Techniques Through the Lens of Minimum Bayes Risk
Amanda Bertsch | Alex Xie | Graham Neubig | Matthew Gormley
Proceedings of the Big Picture Workshop

Minimum Bayes Risk (MBR) decoding is a method for choosing the outputs of a machine learning system based not on the output with the highest probability, but the output with the lowest risk (expected error) among multiple candidates. It is a simple but powerful method: for an additional cost at inference time, MBR provides reliable several-point improvements across metrics for a wide variety of tasks without any additional data or training. Despite this, MBR is not frequently applied in NLP works, and knowledge of the method itself is limited. We first provide an introduction to the method and the recent literature. We show that several recent methods that do not reference MBR can be written as special cases of MBR; this reformulation provides additional theoretical justification for the performance of these methods, explaining some results that were previously only empirical. We provide theoretical and empirical results about the effectiveness of various MBR variants and make concrete recommendations for the application of MBR in NLP models, including future directions in this area.

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Active Retrieval Augmented Generation
Zhengbao Jiang | Frank Xu | Luyu Gao | Zhiqing Sun | Qian Liu | Jane Dwivedi-Yu | Yiming Yang | Jamie Callan | Graham Neubig
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Despite the remarkable ability of large language models (LMs) to comprehend and generate language, they have a tendency to hallucinate and create factually inaccurate output. Augmenting LMs by retrieving information from external knowledge resources is one promising solution. Most existing retrieval augmented LMs employ a retrieve-and-generate setup that only retrieves information once based on the input. This is limiting, however, in more general scenarios involving generation of long texts, where continually gathering information throughout generation is essential. In this work, we provide a generalized view of active retrieval augmented generation, methods that actively decide when and what to retrieve across the course of the generation. We propose Forward-Looking Active REtrieval augmented generation (FLARE), a generic method which iteratively uses a prediction of the upcoming sentence to anticipate future content, which is then utilized as a query to retrieve relevant documents to regenerate the sentence if it contains low-confidence tokens. We test FLARE along with baselines comprehensively over 4 long-form knowledge-intensive generation tasks/datasets. FLARE achieves superior or competitive performance on all tasks, demonstrating the effectiveness of our method.

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CodeBERTScore: Evaluating Code Generation with Pretrained Models of Code
Shuyan Zhou | Uri Alon | Sumit Agarwal | Graham Neubig
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Since the rise of neural natural-language-to-code models (NLCode) that can generate long expressions and statements rather than a single next-token, one of the major problems has been reliably evaluating their generated output. In this paper, we propose CodeBERTScore: an evaluation metric for code generation, which builds on BERTScore (Zhang et al., 2020). Instead of encoding only the generated tokens as in BERTScore, CodeBERTScore also encodes the natural language input preceding the generated code, thus modeling the consistency between the generated code and its given natural language context as well. We perform an extensive evaluation of CodeBERTScore across four programming languages. We find that CodeBERTScore achieves a higher correlation with human preference and with functional correctness than all existing metrics. That is, generated code that receives a higher score by CodeBERTScore is more likely to be preferred by humans, as well as to function correctly when executed. We release five language-specific pretrained models to use with our publicly available code. Our language-specific models have been downloaded more than **1,000,000** times from the Huggingface Hub. Our code and data are available at https://github.com/neulab/code-bert-score

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GlobalBench: A Benchmark for Global Progress in Natural Language Processing
Yueqi Song | Simran Khanuja | Pengfei Liu | Fahim Faisal | Alissa Ostapenko | Genta Winata | Alham Fikri Aji | Samuel Cahyawijaya | Yulia Tsvetkov | Antonios Anastasopoulos | Graham Neubig
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Despite the major advances in NLP, significant disparities in NLP system performance across languages still exist. Arguably, these are due to uneven resource allocation and sub-optimal incentives to work on less resourced languages. To track and further incentivize the global development of equitable language technology, we introduce GlobalBench. Prior multilingual benchmarks are static and have focused on a limited number of tasks and languages. In contrast, GlobalBench is an ever-expanding collection that aims to dynamically track progress on all NLP datasets in all languages. Rather than solely measuring accuracy, GlobalBench also tracks the estimated per-speaker utility and equity of technology across all languages, providing a multi-faceted view of how language technology is serving people of the world. Furthermore, GlobalBench is designed to identify the most under-served languages, and rewards research efforts directed towards those languages. At present, the most under-served languages are the ones with a relatively high population, but nonetheless overlooked by composite multilingual benchmarks (like Punjabi, Portuguese, and Wu Chinese). Currently, GlobalBench covers 966 datasets in 190 languages, and has 1,128 system submissions spanning 62 languages.

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Crossing the Threshold: Idiomatic Machine Translation through Retrieval Augmentation and Loss Weighting
Emmy Liu | Aditi Chaudhary | Graham Neubig
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Idioms are common in everyday language, but often pose a challenge to translators because their meanings do not follow from the meanings of their parts. Despite significant advances, machine translation systems still struggle to translate idiomatic expressions. We provide a simple characterization of idiomatic translation and related issues. This allows us to conduct a synthetic experiment revealing a tipping point at which transformer-based machine translation models correctly default to idiomatic translations. To expand multilingual resources, we compile a dataset of ~4k natural sentences containing idiomatic expressions in French, Finnish, and Japanese. To improve translation of natural idioms, we introduce two straightforward yet effective techniques: the strategic upweighting of training loss on potentially idiomatic sentences, and using retrieval-augmented models. This not only improves the accuracy of a strong pretrained MT model on idiomatic sentences by up to 13% in absolute accuracy, but also holds potential benefits for non-idiomatic sentences.

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Prompt2Model: Generating Deployable Models from Natural Language Instructions
Vijay Viswanathan | Chenyang Zhao | Amanda Bertsch | Tongshuang Wu | Graham Neubig
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Large language models (LLMs) enable system builders today to create competent NLP systems through prompting, where they only need to describe the task in natural language and provide a few examples. However, in other ways, LLMs are a step backward from traditional special-purpose NLP models; they require extensive computational resources for deployment and can be gated behind APIs. In this paper, we propose Prompt2Model, a general-purpose method that takes a natural language task description like the prompts provided to LLMs, and uses it to train a special-purpose model that is conducive to deployment. This is done through a multi-step process of retrieval of existing datasets and pretrained models, dataset generation using LLMs, and supervised fine-tuning on these retrieved and generated datasets. Over three tasks, we demonstrate that given the same few-shot prompt as input, Prompt2Model trains models that outperform the results of a strong LLM, gpt-3.5-turbo, by an average of 20% while being up to 700 times smaller. We also show that this data can be used to obtain reliable performance estimates of model performance, enabling model developers to assess model reliability before deployment. Prompt2Model is available open-source at https://github.com/neulab/prompt2model. Our demo video is posted at youtu.be/LYYQ_EhGd-Q.

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User-Centric Evaluation of OCR Systems for Kwak’wala
Shruti Rijhwani | Daisy Rosenblum | Michayla King | Antonios Anastasopoulos | Graham Neubig
Proceedings of the Sixth Workshop on the Use of Computational Methods in the Study of Endangered Languages

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CTC Alignments Improve Autoregressive Translation
Brian Yan | Siddharth Dalmia | Yosuke Higuchi | Graham Neubig | Florian Metze | Alan W Black | Shinji Watanabe
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Connectionist Temporal Classification (CTC) is a widely used approach for automatic speech recognition (ASR) that performs conditionally independent monotonic alignment. However for translation, CTC exhibits clear limitations due to the contextual and non-monotonic nature of the task and thus lags behind attentional decoder approaches in terms of translation quality. In this work, we argue that CTC does in fact make sense for translation if applied in a joint CTC/attention framework wherein CTC’s core properties can counteract several key weaknesses of pure-attention models during training and decoding. To validate this conjecture, we modify the Hybrid CTC/Attention model originally proposed for ASR to support text-to-text translation (MT) and speech-to-text translation (ST). Our proposed joint CTC/attention models outperform pure-attention baselines across six benchmark translation tasks.

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SigMoreFun Submission to the SIGMORPHON Shared Task on Interlinear Glossing
Taiqi He | Lindia Tjuatja | Nathaniel Robinson | Shinji Watanabe | David R. Mortensen | Graham Neubig | Lori Levin
Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology

In our submission to the SIGMORPHON 2023 Shared Task on interlinear glossing (IGT), we explore approaches to data augmentation and modeling across seven low-resource languages. For data augmentation, we explore two approaches: creating artificial data from the provided training data and utilizing existing IGT resources in other languages. On the modeling side, we test an enhanced version of the provided token classification baseline as well as a pretrained multilingual seq2seq model. Additionally, we apply post-correction using a dictionary for Gitksan, the language with the smallest amount of data. We find that our token classification models are the best performing, with the highest word-level accuracy for Arapaho and highest morpheme-level accuracy for Gitksan out of all submissions. We also show that data augmentation is an effective strategy, though applying artificial data pretraining has very different effects across both models tested.

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Improving Factuality of Abstractive Summarization via Contrastive Reward Learning
I-chun Chern | Zhiruo Wang | Sanjan Das | Bhavuk Sharma | Pengfei Liu | Graham Neubig
Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023)

Modern abstractive summarization models often generate summaries that contain hallucinated or contradictory information. In this paper, we propose a simple but effective contrastive learning framework that incorporates recent developments in reward learning and factuality metrics. Empirical studies demonstrate that the proposed framework enables summarization models to learn from feedback of factuality metrics using contrastive reward learning, leading to more factual summaries by human evaluations. This suggests that further advances in learning and evaluation algorithms can feed directly into providing more factual summaries. Code and human evaluation results will be publicly available at \url{https://github.com/EthanC111/factuality_summarization}.

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Syntax and Semantics Meet in the “Middle”: Probing the Syntax-Semantics Interface of LMs Through Agentivity
Lindia Tjuatja | Emmy Liu | Lori Levin | Graham Neubig
Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)

Recent advances in large language models have prompted researchers to examine their abilities across a variety of linguistic tasks, but little has been done to investigate how models handle the interactions in meaning across words and larger syntactic forms—i.e. phenomena at the intersection of syntax and semantics. We present the semantic notion of agentivity as a case study for probing such interactions. We created a novel evaluation dataset by utilitizing the unique linguistic properties of a subset of optionally transitive English verbs. This dataset was used to prompt varying sizes of three model classes to see if they are sensitive to agentivity at the lexical level, and if they can appropriately employ these word-level priors given a specific syntactic context. Overall, GPT-3 text-davinci-003 performs extremely well across all experiments, outperforming all other models tested by far. In fact, the results are even better correlated with human judgements than both syntactic and semantic corpus statistics. This suggests that LMs may potentially serve as more useful tools for linguistic annotation, theory testing, and discovery than select corpora for certain tasks.

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Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation
Patrick Fernandes | Aman Madaan | Emmy Liu | António Farinhas | Pedro Henrique Martins | Amanda Bertsch | José G. C. de Souza | Shuyan Zhou | Tongshuang Wu | Graham Neubig | André F. T. Martins
Transactions of the Association for Computational Linguistics, Volume 11

Natural language generation has witnessed significant advancements due to the training of large language models on vast internet-scale datasets. Despite these advancements, there exists a critical challenge: These models can inadvertently generate content that is toxic, inaccurate, and unhelpful, and existing automatic evaluation metrics often fall short of identifying these shortcomings. As models become more capable, human feedback is an invaluable signal for evaluating and improving models. This survey aims to provide an overview of recent research that has leveraged human feedback to improve natural language generation. First, we introduce a taxonomy distilled from existing research to categorize and organize the varied forms of feedback. Next, we discuss how feedback can be described by its format and objective, and cover the two approaches proposed to use feedback (either for training or decoding): directly using feedback or training feedback models. We also discuss existing datasets for human-feedback data collection, and concerns surrounding feedback collection. Finally, we provide an overview of the nascent field of AI feedback, which uses large language models to make judgments based on a set of principles and minimize the need for human intervention. We also release a website of this survey at feedback-gap-survey.info.

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ChatGPT MT: Competitive for High- (but Not Low-) Resource Languages
Nathaniel Robinson | Perez Ogayo | David R. Mortensen | Graham Neubig
Proceedings of the Eighth Conference on Machine Translation

Large language models (LLMs) implicitly learn to perform a range of language tasks, including machine translation (MT). Previous studies explore aspects of LLMs’ MT capabilities. However, there exist a wide variety of languages for which recent LLM MT performance has never before been evaluated. Without published experimental evidence on the matter, it is difficult for speakers of the world’s diverse languages to know how and whether they can use LLMs for their languages. We present the first experimental evidence for an expansive set of 204 languages, along with MT cost analysis, using the FLORES-200 benchmark. Trends reveal that GPT models approach or exceed traditional MT model performance for some high-resource languages (HRLs) but consistently lag for low-resource languages (LRLs), under-performing traditional MT for 84.1% of languages we covered. Our analysis reveals that a language’s resource level is the most important feature in determining ChatGPT’s relative ability to translate it, and suggests that ChatGPT is especially disadvantaged for LRLs and African languages.

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The Devil Is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation
Patrick Fernandes | Daniel Deutsch | Mara Finkelstein | Parker Riley | André Martins | Graham Neubig | Ankush Garg | Jonathan Clark | Markus Freitag | Orhan Firat
Proceedings of the Eighth Conference on Machine Translation

Automatic evaluation of machine translation (MT) is a critical tool driving the rapid iterative development of MT systems. While considerable progress has been made on estimating a single scalar quality score, current metrics lack the informativeness of more detailed schemes that annotate individual errors, such as Multidimensional Quality Metrics (MQM). In this paper, we help fill this gap by proposing AutoMQM, a prompting technique which leverages the reasoning and in-context learning capabilities of large language models (LLMs) and asks them to identify and categorize errors in translations. We start by evaluating recent LLMs, such as PaLM and PaLM-2, through simple score prediction prompting, and we study the impact of labeled data through in-context learning and finetuning. We then evaluate AutoMQM with PaLM-2 models, and we find that it improves performance compared to just prompting for scores (with particularly large gains for larger models) while providing interpretability through error spans that align with human annotations.

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Neural Machine Translation for the Indigenous Languages of the Americas: An Introduction
Manuel Mager | Rajat Bhatnagar | Graham Neubig | Ngoc Thang Vu | Katharina Kann
Proceedings of the Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP)

Neural models have drastically advanced state of the art for machine translation (MT) between high-resource languages. Traditionally, these models rely on large amounts of training data, but many language pairs lack these resources. However, an important part of the languages in the world do not have this amount of data. Most languages from the Americas are among them, having a limited amount of parallel and monolingual data, if any. Here, we present an introduction to the interested reader to the basic challenges, concepts, and techniques that involve the creation of MT systems for these languages. Finally, we discuss the recent advances and findings and open questions, product of an increased interest of the NLP community in these languages.

2022

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OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering
Zhengbao Jiang | Yi Mao | Pengcheng He | Graham Neubig | Weizhu Chen
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

The information in tables can be an important complement to text, making table-based question answering (QA) systems of great value. The intrinsic complexity of handling tables often adds an extra burden to both model design and data annotation. In this paper, we aim to develop a simple table-based QA model with minimal annotation effort. Motivated by the fact that table-based QA requires both alignment between questions and tables and the ability to perform complicated reasoning over multiple table elements, we propose an omnivorous pretraining approach that consumes both natural and synthetic data to endow models with these respective abilities. Specifically, given freely available tables, we leverage retrieval to pair them with relevant natural sentences for mask-based pretraining, and synthesize NL questions by converting SQL sampled from tables for pretraining with a QA loss. We perform extensive experiments in both few-shot and full settings, and the results clearly demonstrate the superiority of our model OmniTab, with the best multitasking approach achieving an absolute gain of 16.2% and 2.7% in 128-shot and full settings respectively, also establishing a new state-of-the-art on WikiTableQuestions. Detailed ablations and analyses reveal different characteristics of natural and synthetic data, shedding light on future directions in omnivorous pretraining.

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Quality-Aware Decoding for Neural Machine Translation
Patrick Fernandes | António Farinhas | Ricardo Rei | José G. C. de Souza | Perez Ogayo | Graham Neubig | Andre Martins
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Despite the progress in machine translation quality estimation and evaluation in the last years, decoding in neural machine translation (NMT) is mostly oblivious to this and centers around finding the most probable translation according to the model (MAP decoding), approximated with beam search. In this paper, we bring together these two lines of research and propose quality-aware decoding for NMT, by leveraging recent breakthroughs in reference-free and reference-based MT evaluation through various inference methods like N-best reranking and minimum Bayes risk decoding. We perform an extensive comparison of various possible candidate generation and ranking methods across four datasets and two model classes and find that quality-aware decoding consistently outperforms MAP-based decoding according both to state-of-the-art automatic metrics (COMET and BLEURT) and to human assessments.

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Testing the Ability of Language Models to Interpret Figurative Language
Emmy Liu | Chenxuan Cui | Kenneth Zheng | Graham Neubig
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Figurative and metaphorical language are commonplace in discourse, and figurative expressions play an important role in communication and cognition. However, figurative language has been a relatively under-studied area in NLP, and it remains an open question to what extent modern language models can interpret nonliteral phrases. To address this question, we introduce Fig-QA, a Winograd-style nonliteral language understanding task consisting of correctly interpreting paired figurative phrases with divergent meanings. We evaluate the performance of several state-of-the-art language models on this task, and find that although language models achieve performance significantly over chance, they still fall short of human performance, particularly in zero- or few-shot settings. This suggests that further work is needed to improve the nonliteral reasoning capabilities of language models.

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Expanding Pretrained Models to Thousands More Languages via Lexicon-based Adaptation
Xinyi Wang | Sebastian Ruder | Graham Neubig
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The performance of multilingual pretrained models is highly dependent on the availability of monolingual or parallel text present in a target language. Thus, the majority of the world’s languages cannot benefit from recent progress in NLP as they have no or limited textual data. To expand possibilities of using NLP technology in these under-represented languages, we systematically study strategies that relax the reliance on conventional language resources through the use of bilingual lexicons, an alternative resource with much better language coverage. We analyze different strategies to synthesize textual or labeled data using lexicons, and how this data can be combined with monolingual or parallel text when available. For 19 under-represented languages across 3 tasks, our methods lead to consistent improvements of up to 5 and 15 points with and without extra monolingual text respectively. Overall, our study highlights how NLP methods can be adapted to thousands more languages that are under-served by current technology.

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On The Ingredients of an Effective Zero-shot Semantic Parser
Pengcheng Yin | John Wieting | Avirup Sil | Graham Neubig
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Semantic parsers map natural language utterances into meaning representations (e.g., programs). Such models are typically bottlenecked by the paucity of training data due to the required laborious annotation efforts. Recent studies have performed zero-shot learning by synthesizing training examples of canonical utterances and programs from a grammar, and further paraphrasing these utterances to improve linguistic diversity. However, such synthetic examples cannot fully capture patterns in real data. In this paper we analyze zero-shot parsers through the lenses of the language and logical gaps (Herzig and Berant, 2019), which quantify the discrepancy of language and programmatic patterns between the canonical examples and real-world user-issued ones. We propose bridging these gaps using improved grammars, stronger paraphrasers, and efficient learning methods using canonical examples that most likely reflect real user intents. Our model achieves strong performance on two semantic parsing benchmarks (Scholar, Geo) with zero labeled data.

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DEEP: DEnoising Entity Pre-training for Neural Machine Translation
Junjie Hu | Hiroaki Hayashi | Kyunghyun Cho | Graham Neubig
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

It has been shown that machine translation models usually generate poor translations for named entities that are infrequent in the training corpus. Earlier named entity translation methods mainly focus on phonetic transliteration, which ignores the sentence context for translation and is limited in domain and language coverage. To address this limitation, we propose DEEP, a DEnoising Entity Pre-training method that leverages large amounts of monolingual data and a knowledge base to improve named entity translation accuracy within sentences. Besides, we investigate a multi-task learning strategy that finetunes a pre-trained neural machine translation model on both entity-augmented monolingual data and parallel data to further improve entity translation. Experimental results on three language pairs demonstrate that DEEP results in significant improvements over strong denoising auto-encoding baselines, with a gain of up to 1.3 BLEU and up to 9.2 entity accuracy points for English-Russian translation.

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BRIO: Bringing Order to Abstractive Summarization
Yixin Liu | Pengfei Liu | Dragomir Radev | Graham Neubig
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Abstractive summarization models are commonly trained using maximum likelihood estimation, which assumes a deterministic (one-point) target distribution in which an ideal model will assign all the probability mass to the reference summary. This assumption may lead to performance degradation during inference, where the model needs to compare several system-generated (candidate) summaries that have deviated from the reference summary. To address this problem, we propose a novel training paradigm which assumes a non-deterministic distribution so that different candidate summaries are assigned probability mass according to their quality. Our method achieves a new state-of-the-art result on the CNN/DailyMail (47.78 ROUGE-1) and XSum (49.07 ROUGE-1) datasets. Further analysis also shows that our model can estimate probabilities of candidate summaries that are more correlated with their level of quality.

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Show Me More Details: Discovering Hierarchies of Procedures from Semi-structured Web Data
Shuyan Zhou | Li Zhang | Yue Yang | Qing Lyu | Pengcheng Yin | Chris Callison-Burch | Graham Neubig
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Procedures are inherently hierarchical. To “make videos”, one may need to “purchase a camera”, which in turn may require one to “set a budget”. While such hierarchical knowledge is critical for reasoning about complex procedures, most existing work has treated procedures as shallow structures without modeling the parent-child relation. In this work, we attempt to construct an open-domain hierarchical knowledge-base (KB) of procedures based on wikiHow, a website containing more than 110k instructional articles, each documenting the steps to carry out a complex procedure. To this end, we develop a simple and efficient method that links steps (e.g., “purchase a camera”) in an article to other articles with similar goals (e.g., “how to choose a camera”), recursively constructing the KB. Our method significantly outperforms several strong baselines according to automatic evaluation, human judgment, and application to downstream tasks such as instructional video retrieval.

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Systematic Inequalities in Language Technology Performance across the World’s Languages
Damian Blasi | Antonios Anastasopoulos | Graham Neubig
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Natural language processing (NLP) systems have become a central technology in communication, education, medicine, artificial intelligence, and many other domains of research and development. While the performance of NLP methods has grown enormously over the last decade, this progress has been restricted to a minuscule subset of the world’s 6,500 languages. We introduce a framework for estimating the global utility of language technologies as revealed in a comprehensive snapshot of recent publications in NLP. Our analyses involve the field at large, but also more in-depth studies on both user-facing technologies (machine translation, language understanding, question answering, text-to-speech synthesis) as well as foundational NLP tasks (dependency parsing, morphological inflection). In the process, we (1) quantify disparities in the current state of NLP research, (2) explore some of its associated societal and academic factors, and (3) produce tailored recommendations for evidence-based policy making aimed at promoting more global and equitable language technologies. Data and code to reproduce the findings discussed in this paper areavailable on GitHub (https://github.com/neubig/globalutility).

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AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages
Abteen Ebrahimi | Manuel Mager | Arturo Oncevay | Vishrav Chaudhary | Luis Chiruzzo | Angela Fan | John Ortega | Ricardo Ramos | Annette Rios | Ivan Vladimir Meza Ruiz | Gustavo Giménez-Lugo | Elisabeth Mager | Graham Neubig | Alexis Palmer | Rolando Coto-Solano | Thang Vu | Katharina Kann
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Pretrained multilingual models are able to perform cross-lingual transfer in a zero-shot setting, even for languages unseen during pretraining. However, prior work evaluating performance on unseen languages has largely been limited to low-level, syntactic tasks, and it remains unclear if zero-shot learning of high-level, semantic tasks is possible for unseen languages. To explore this question, we present AmericasNLI, an extension of XNLI (Conneau et al., 2018) to 10 Indigenous languages of the Americas. We conduct experiments with XLM-R, testing multiple zero-shot and translation-based approaches. Additionally, we explore model adaptation via continued pretraining and provide an analysis of the dataset by considering hypothesis-only models. We find that XLM-R’s zero-shot performance is poor for all 10 languages, with an average performance of 38.48%. Continued pretraining offers improvements, with an average accuracy of 43.85%. Surprisingly, training on poorly translated data by far outperforms all other methods with an accuracy of 49.12%.

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DataLab: A Platform for Data Analysis and Intervention
Yang Xiao | Jinlan Fu | Weizhe Yuan | Vijay Viswanathan | Zhoumianze Liu | Yixin Liu | Graham Neubig | Pengfei Liu
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

Despite data’s crucial role in machine learning, most existing tools and research tend to focus on systems on top of existing data rather than how to interpret and manipulate data. In this paper, we propose DataLab, a unified data-oriented platform that not only allows users to interactively analyze the characteristics of data but also provides a standardized interface so that many data processing operations can be provided within a unified interface. Additionally, in view of the ongoing surge in the proliferation of datasets, DataLab has features for dataset recommendation and global vision analysis that help researchers form a better view of the data ecosystem. So far, DataLab covers 1,300 datasets and 3,583 of its transformed version, where 313 datasets support different types of analysis (e.g., with respect to gender bias) with the help of 119M samples annotated by 318 feature functions. DataLab is under active development and will be supported going forward. We have released a web platform, web API, Python SDK, and PyPI published package, which hopefully, can meet the diverse needs of researchers.

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Interpreting Language Models with Contrastive Explanations
Kayo Yin | Graham Neubig
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Model interpretability methods are often used to explain NLP model decisions on tasks such as text classification, where the output space is relatively small. However, when applied to language generation, where the output space often consists of tens of thousands of tokens, these methods are unable to provide informative explanations. Language models must consider various features to predict a token, such as its part of speech, number, tense, or semantics.Existing explanation methods conflate evidence for all these features into a single explanation, which is less interpretable for human understanding.To disentangle the different decisions in language modeling, we focus on explaining language models contrastively: we look for salient input tokens that explain why the model predicted one token instead of another. We demonstrate that contrastive explanations are quantifiably better than non-contrastive explanations in verifying major grammatical phenomena, and that they significantly improve contrastive model simulatability for human observers. We also identify groups of contrastive decisions where the model uses similar evidence, and we are able to characterize what input tokens models use during various language generation decisions.

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Language Models of Code are Few-Shot Commonsense Learners
Aman Madaan | Shuyan Zhou | Uri Alon | Yiming Yang | Graham Neubig
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

We address the general task of structured commonsense reasoning: given a natural language input, the goal is to generate a graph such as an event or a reasoning-graph.To employ large language models (LMs) for this task, existing approaches ‘serialize’ the output graph as a flat list of nodes and edges.Although feasible, these serialized graphs strongly deviate from the natural language corpora that LMs were pre-trained on, hindering LMs from generating them correctly. In this paper, we show that when we instead frame structured commonsense reasoning tasks as code generation tasks, pre-trained LMs of code are better structured commonsense reasoners than LMs of natural language, even when the downstream task does not involve source code at all.We demonstrate our approach across three diverse structured commonsense reasoning tasks. In all these natural language tasks, we show that using our approach, a code generation LM (codex) outperforms natural-LMs that are fine-tuned on the target task (T5) and other strong LMs such as GPT-3 in the few-shot setting.

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Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer
Zhengbao Jiang | Luyu Gao | Zhiruo Wang | Jun Araki | Haibo Ding | Jamie Callan | Graham Neubig
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Systems for knowledge-intensive tasks such as open-domain question answering (QA) usually consist of two stages: efficient retrieval of relevant documents from a large corpus and detailed reading of the selected documents. This is usually done through two separate models, a retriever that encodes the query and finds nearest neighbors, and a reader based on Transformers. These two components are usually modeled separately, which necessitates a cumbersome implementation and is awkward to optimize in an end-to-end fashion. In this paper, we revisit this design and eschew the separate architecture and training in favor of a single Transformer that performs retrieval as attention (RAA), and end-to-end training solely based on supervision from the end QA task. We demonstrate for the first time that an end-to-end trained single Transformer can achieve both competitive retrieval and QA performance on in-domain datasets, matching or even slightly outperforming state-of-the-art dense retrievers and readers. Moreover, end-to-end adaptation of our model significantly boosts its performance on out-of-domain datasets in both supervised and unsupervised settings, making our model a simple and adaptable end-to-end solution for knowledge-intensive tasks.

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MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition
David Adelani | Graham Neubig | Sebastian Ruder | Shruti Rijhwani | Michael Beukman | Chester Palen-Michel | Constantine Lignos | Jesujoba Alabi | Shamsuddeen Muhammad | Peter Nabende | Cheikh M. Bamba Dione | Andiswa Bukula | Rooweither Mabuya | Bonaventure F. P. Dossou | Blessing Sibanda | Happy Buzaaba | Jonathan Mukiibi | Godson Kalipe | Derguene Mbaye | Amelia Taylor | Fatoumata Kabore | Chris Chinenye Emezue | Anuoluwapo Aremu | Perez Ogayo | Catherine Gitau | Edwin Munkoh-Buabeng | Victoire Memdjokam Koagne | Allahsera Auguste Tapo | Tebogo Macucwa | Vukosi Marivate | Mboning Tchiaze Elvis | Tajuddeen Gwadabe | Tosin Adewumi | Orevaoghene Ahia | Joyce Nakatumba-Nabende
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

African languages are spoken by over a billion people, but they are under-represented in NLP research and development. Multiple challenges exist, including the limited availability of annotated training and evaluation datasets as well as the lack of understanding of which settings, languages, and recently proposed methods like cross-lingual transfer will be effective. In this paper, we aim to move towards solutions for these challenges, focusing on the task of named entity recognition (NER). We present the creation of the largest to-date human-annotated NER dataset for 20 African languages. We study the behaviour of state-of-the-art cross-lingual transfer methods in an Africa-centric setting, empirically demonstrating that the choice of source transfer language significantly affects performance. While much previous work defaults to using English as the source language, our results show that choosing the best transfer language improves zero-shot F1 scores by an average of 14% over 20 languages as compared to using English.

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Are representations built from the ground up? An empirical examination of local composition in language models
Emmy Liu | Graham Neubig
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Compositionality, the phenomenon where the meaning of a phrase can be derived from its constituent parts, is a hallmark of human language. At the same time, many phrases are non-compositional, carrying a meaning beyond that of each part in isolation. Representing both of these types of phrases is critical for language understanding, but it is an open question whether modern language models (LMs) learn to do so; in this work we examine this question. We first formulate a problem of predicting the LM-internal representations of longer phrases given those of their constituents. We find that the representation of a parent phrase can be predicted with some accuracy given an affine transformation of its children. While we would expect the predictive accuracy to correlate with human judgments of semantic compositionality, we find this is largely not the case, indicating that LMs may not accurately distinguish between compositional and non-compositional phrases. We perform a variety of analyses, shedding light on when different varieties of LMs do and do not generate compositional representations, and discuss implications for future modeling work.

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English Contrastive Learning Can Learn Universal Cross-lingual Sentence Embeddings
Yaushian Wang | Ashley Wu | Graham Neubig
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Universal cross-lingual sentence embeddings map semantically similar cross-lingual sentences into a shared embedding space. Aligning cross-lingual sentence embeddings usually requires supervised cross-lingual parallel sentences. In this work, we propose mSimCSE, which extends SimCSE to multilingual settings and reveal that contrastive learning on English data can surprisingly learn high-quality universal cross-lingual sentence embeddings without any parallel data.In unsupervised and weakly supervised settings, mSimCSE significantly improves previous sentence embedding methods on cross-lingual retrieval and multilingual STS tasks. The performance of unsupervised mSimCSE is comparable to fully supervised methods in retrieving low-resource languages and multilingual STS.The performance can be further enhanced when cross-lingual NLI data is available.

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KGxBoard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models
Haris Widjaja | Kiril Gashteovski | Wiem Ben Rim | Pengfei Liu | Christopher Malon | Daniel Ruffinelli | Carolin Lawrence | Graham Neubig
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Knowledge Graphs (KGs) store information in the form of (head, predicate, tail)-triples. To augment KGs with new knowledge, researchers proposed models for KG Completion (KGC) tasks such as link prediction; i.e., answering (h; p; ?) or (?; p; t) queries. Such models are usually evaluated with averaged metrics on a held-out test set. While useful for tracking progress, averaged single-score metrics cannotreveal what exactly a model has learned — or failed to learn. To address this issue, we propose KGxBoard: an interactive framework for performing fine-grained evaluation on meaningful subsets of the data, each of which tests individual and interpretable capabilities of a KGC model. In our experiments, we highlight the findings that we discovered with the use of KGxBoard, which would have been impossible to detect with standard averaged single-score metrics.

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Paraphrastic Representations at Scale
John Wieting | Kevin Gimpel | Graham Neubig | Taylor Berg-kirkpatrick
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We present a system that allows users to train their own state-of-the-art paraphrastic sentence representations in a variety of languages. We release trained models for English, Arabic, German, Spanish, French, Russian, Turkish, and Chinese. We train these models on large amounts of data, achieving significantly improved performance from our original papers on a suite of monolingual semantic similarity, cross-lingual semantic similarity, and bitext mining tasks. Moreover, the resulting models surpass all prior work on efficient unsupervised semantic textual similarity, even significantly outperforming supervised BERT-based models like Sentence-BERT (Reimers and Gurevych, 2019). Most importantly, our models are orders of magnitude faster than other strong similarity models and can be used on CPU with little difference in inference speed (even improved speed over GPU when using more CPU cores), making these models an attractive choice for users without access to GPUs or for use on embedded devices. Finally, we add significantly increased functionality to the code bases for training paraphrastic sentence models, easing their use for both inference and for training them for any desired language with parallel data. We also include code to automatically download and preprocess training data.

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Breaking Down Multilingual Machine Translation
Ting-Rui Chiang | Yi-Pei Chen | Yi-Ting Yeh | Graham Neubig
Findings of the Association for Computational Linguistics: ACL 2022

While multilingual training is now an essential ingredient in machine translation (MT) systems, recent work has demonstrated that it has different effects in different multilingual settings, such as many-to-one, one-to-many, and many-to-many learning. These training settings expose the encoder and the decoder in a machine translation model with different data distributions. In this paper, we examine how different varieties of multilingual training contribute to learning these two components of the MT model. Specifically, we compare bilingual models with encoders and/or decoders initialized by multilingual training. We show that multilingual training is beneficial to encoders in general, while it only benefits decoders for low-resource languages (LRLs). We further find the important attention heads for each language pair and compare their correlations during inference. Our analysis sheds light on how multilingual translation models work and also enables us to propose methods to improve performance by training with highly related languages. Our many-to-one models for high-resource languages and one-to-many models for LRL outperform the best results reported by Aharoni et al. (2019).

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Prompt Consistency for Zero-Shot Task Generalization
Chunting Zhou | Junxian He | Xuezhe Ma | Taylor Berg-Kirkpatrick | Graham Neubig
Findings of the Association for Computational Linguistics: EMNLP 2022

One of the most impressive results of recent NLP history is the ability of pre-trained language models to solve new tasks in a zero-shot setting. To achieve this, NLP tasks are framed as natural language prompts, generating a response indicating the predicted output. Nonetheless, the performance in such settings often lags far behind its supervised counterpart, suggesting a large space for potential improvement. In this paper, we explore methods to utilize unlabeled data to improve zero-shot performance. Specifically, we take advantage of the fact that multiple prompts can be used to specify a single task, and propose to regularize prompt consistency, encouraging consistent predictions over this diverse set of prompts. Our method makes it possible to fine-tune the model either with extra unlabeled training data, or directly on test input at inference time in an unsupervised manner. In experiments, our approach outperforms the state-of-the-art zero-shot learner, T0, on 9 out of 11 datasets across 4 NLP tasks by up to 10.6 absolute points in terms of accuracy. The gains are often attained with a small number of unlabeled examples.

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Learning to Model Editing Processes
Machel Reid | Graham Neubig
Findings of the Association for Computational Linguistics: EMNLP 2022

Most existing sequence generation models produce outputs in one pass, usually left-to-right. However, this is in contrast with a more natural approach that humans use in generating content; iterative refinement and editing. Recent work has introduced edit-based models for various tasks (such as neural machine translation and text style transfer), but these generally model a single edit step. In this work, we propose modeling editing processes, modeling the whole process of iteratively generating sequences. We form a conceptual framework to describe the likelihood of multi-step edits, and describe neural models that can learn a generative model of sequences based on these multistep edits. We introduce baseline results and metrics on this task, finding that modeling editing processes improves performance on a variety of axes on both our proposed task and related downstream tasks compared to previous single-step models of edits.

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He Said, She Said: Style Transfer for Shifting the Perspective of Dialogues
Amanda Bertsch | Graham Neubig | Matthew R. Gormley
Findings of the Association for Computational Linguistics: EMNLP 2022

In this work, we define a new style transfer task: perspective shift, which reframes a dialouge from informal first person to a formal third person rephrasing of the text. This task requires challenging coreference resolution, emotion attribution, and interpretation of informal text. We explore several baseline approaches and discuss further directions on this task when applied to short dialogues. As a sample application, we demonstrate that applying perspective shifting to a dialogue summarization dataset (SAMSum) substantially improves the zero-shot performance of extractive news summarization models on this data. Additionally, supervised extractive models perform better when trained on perspective shifted data than on the original dialogues. We release our code publicly.

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Proceedings of the First Workshop on Scaling Up Multilingual Evaluation
Kabir Ahuja | Antonios Anastasopoulos | Barun Patra | Graham Neubig | Monojit Choudhury | Sandipan Dandapat | Sunayana Sitaram | Vishrav Chaudhary
Proceedings of the First Workshop on Scaling Up Multilingual Evaluation

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The SUMEval 2022 Shared Task on Performance Prediction of Multilingual Pre-trained Language Models
Kabir Ahuja | Antonios Anastasopoulos | Barun Patra | Graham Neubig | Monojit Choudhury | Sandipan Dandapat | Sunayana Sitaram | Vishrav Chaudhary
Proceedings of the First Workshop on Scaling Up Multilingual Evaluation

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Table Retrieval May Not Necessitate Table-specific Model Design
Zhiruo Wang | Zhengbao Jiang | Eric Nyberg | Graham Neubig
Proceedings of the Workshop on Structured and Unstructured Knowledge Integration (SUKI)

Tables are an important form of structured data for both human and machine readers alike, providing answers to questions that cannot, or cannot easily, be found in texts. Recent work has designed special models and training paradigms for table-related tasks such as table-based question answering and table retrieval. Though effective, they add complexity in both modeling and data acquisition compared to generic text solutions and obscure which elements are truly beneficial. In this work, we focus on the task of table retrieval, and ask: “is table-specific model design necessary for table retrieval, or can a simpler text-based model be effectively used to achieve a similar result?’’ First, we perform an analysis on a table-based portion of the Natural Questions dataset (NQ-table), and find that structure plays a negligible role in more than 70% of the cases. Based on this, we experiment with a general Dense Passage Retriever (DPR) based on text and a specialized Dense Table Retriever (DTR) that uses table-specific model designs. We find that DPR performs well without any table-specific design and training, and even achieves superior results compared to DTR when fine-tuned on properly linearized tables. We then experiment with three modules to explicitly encode table structures, namely auxiliary row/column embeddings, hard attention masks, and soft relation-based attention biases. However, none of these yielded significant improvements, suggesting that table-specific model design may not be necessary for table retrieval.

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Hierarchical Control of Situated Agents through Natural Language
Shuyan Zhou | Pengcheng Yin | Graham Neubig
Proceedings of the Workshop on Structured and Unstructured Knowledge Integration (SUKI)

When humans perform a particular task, they do so hierarchically: splitting higher-level tasks into smaller sub-tasks. However, most works on natural language (NL) command of situated agents have treated the procedures to be executed as flat sequences of simple actions, or any hierarchies of procedures have been shallow at best. In this paper, we propose a formalism of procedures as programs, a method for representing hierarchical procedural knowledge for agent command and control aimed at enabling easy application to various scenarios. We further propose a modeling paradigm of hierarchical modular networks, which consist of a planner and reactors that convert NL intents to predictions of executable programs and probe the environment for information necessary to complete the program execution. We instantiate this framework on the IQA and ALFRED datasets for NL instruction following. Our model outperforms reactive baselines by a large margin on both datasets. We also demonstrate that our framework is more data-efficient, and that it allows for fast iterative development.

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Evaluating Explanations: How Much Do Explanations from the Teacher Aid Students?
Danish Pruthi | Rachit Bansal | Bhuwan Dhingra | Livio Baldini Soares | Michael Collins | Zachary C. Lipton | Graham Neubig | William W. Cohen
Transactions of the Association for Computational Linguistics, Volume 10

While many methods purport to explain predictions by highlighting salient features, what aims these explanations serve and how they ought to be evaluated often go unstated. In this work, we introduce a framework to quantify the value of explanations via the accuracy gains that they confer on a student model trained to simulate a teacher model. Crucially, the explanations are available to the student during training, but are not available at test time. Compared with prior proposals, our approach is less easily gamed, enabling principled, automatic, model-agnostic evaluation of attributions. Using our framework, we compare numerous attribution methods for text classification and question answering, and observe quantitative differences that are consistent (to a moderate to high degree) across different student model architectures and learning strategies.1

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CMU’s IWSLT 2022 Dialect Speech Translation System
Brian Yan | Patrick Fernandes | Siddharth Dalmia | Jiatong Shi | Yifan Peng | Dan Berrebbi | Xinyi Wang | Graham Neubig | Shinji Watanabe
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)

This paper describes CMU’s submissions to the IWSLT 2022 dialect speech translation (ST) shared task for translating Tunisian-Arabic speech to English text. We use additional paired Modern Standard Arabic data (MSA) to directly improve the speech recognition (ASR) and machine translation (MT) components of our cascaded systems. We also augment the paired ASR data with pseudo translations via sequence-level knowledge distillation from an MT model and use these artificial triplet ST data to improve our end-to-end (E2E) systems. Our E2E models are based on the Multi-Decoder architecture with searchable hidden intermediates. We extend the Multi-Decoder by orienting the speech encoder towards the target language by applying ST supervision as hierarchical connectionist temporal classification (CTC) multi-task. During inference, we apply joint decoding of the ST CTC and ST autoregressive decoder branches of our modified Multi-Decoder. Finally, we apply ROVER voting, posterior combination, and minimum bayes-risk decoding with combined N-best lists to ensemble our various cascaded and E2E systems. Our best systems reached 20.8 and 19.5 BLEU on test2 (blind) and test1 respectively. Without any additional MSA data, we reached 20.4 and 19.2 on the same test sets.

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Understanding and Improving Zero-shot Multi-hop Reasoning in Generative Question Answering
Zhengbao Jiang | Jun Araki | Haibo Ding | Graham Neubig
Proceedings of the 29th International Conference on Computational Linguistics

Generative question answering (QA) models generate answers to questions either solely based on the parameters of the model (the closed-book setting) or additionally retrieving relevant evidence (the open-book setting). Generative QA models can answer some relatively complex questions, but the mechanism through which they do so is still poorly understood. We perform several studies aimed at better understanding the multi-hop reasoning capabilities of generative QA models. First, we decompose multi-hop questions into multiple corresponding single-hop questions, and find marked inconsistency in QA models’ answers on these pairs of ostensibly identical question chains. Second, we find that models lack zero-shot multi-hop reasoning ability: when trained only on single-hop questions, models generalize poorly to multi-hop questions. Finally, we demonstrate that it is possible to improve models’ zero-shot multi-hop reasoning capacity through two methods that approximate real multi-hop natural language (NL) questions by training on either concatenation of single-hop questions or logical forms (SPARQL). In sum, these results demonstrate that multi-hop reasoning does not emerge naturally in generative QA models, but can be encouraged by advances in training or modeling techniques. Code is available at https://github.com/jzbjyb/multihop.

2021

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CitationIE: Leveraging the Citation Graph for Scientific Information Extraction
Vijay Viswanathan | Graham Neubig | Pengfei Liu
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Automatically extracting key information from scientific documents has the potential to help scientists work more efficiently and accelerate the pace of scientific progress. Prior work has considered extracting document-level entity clusters and relations end-to-end from raw scientific text, which can improve literature search and help identify methods and materials for a given problem. Despite the importance of this task, most existing works on scientific information extraction (SciIE) consider extraction solely based on the content of an individual paper, without considering the paper’s place in the broader literature. In contrast to prior work, we augment our text representations by leveraging a complementary source of document context: the citation graph of referential links between citing and cited papers. On a test set of English-language scientific documents, we show that simple ways of utilizing the structure and content of the citation graph can each lead to significant gains in different scientific information extraction tasks. When these tasks are combined, we observe a sizable improvement in end-to-end information extraction over the state-of-the-art, suggesting the potential for future work along this direction. We release software tools to facilitate citation-aware SciIE development.

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Do Context-Aware Translation Models Pay the Right Attention?
Kayo Yin | Patrick Fernandes | Danish Pruthi | Aditi Chaudhary | André F. T. Martins | Graham Neubig
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Context-aware machine translation models are designed to leverage contextual information, but often fail to do so. As a result, they inaccurately disambiguate pronouns and polysemous words that require context for resolution. In this paper, we ask several questions: What contexts do human translators use to resolve ambiguous words? Are models paying large amounts of attention to the same context? What if we explicitly train them to do so? To answer these questions, we introduce SCAT (Supporting Context for Ambiguous Translations), a new English-French dataset comprising supporting context words for 14K translations that professional translators found useful for pronoun disambiguation. Using SCAT, we perform an in-depth analysis of the context used to disambiguate, examining positional and lexical characteristics of the supporting words. Furthermore, we measure the degree of alignment between the model’s attention scores and the supporting context from SCAT, and apply a guided attention strategy to encourage agreement between the two.

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Measuring and Increasing Context Usage in Context-Aware Machine Translation
Patrick Fernandes | Kayo Yin | Graham Neubig | André F. T. Martins
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Recent work in neural machine translation has demonstrated both the necessity and feasibility of using inter-sentential context, context from sentences other than those currently being translated. However, while many current methods present model architectures that theoretically can use this extra context, it is often not clear how much they do actually utilize it at translation time. In this paper, we introduce a new metric, conditional cross-mutual information, to quantify usage of context by these models. Using this metric, we measure how much document-level machine translation systems use particular varieties of context. We find that target context is referenced more than source context, and that including more context has a diminishing affect on results. We then introduce a new, simple training method, context-aware word dropout, to increase the usage of context by context-aware models. Experiments show that our method not only increases context usage, but also improves the translation quality according to metrics such as BLEU and COMET, as well as performance on anaphoric pronoun resolution and lexical cohesion contrastive datasets.

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ExplainaBoard: An Explainable Leaderboard for NLP
Pengfei Liu | Jinlan Fu | Yang Xiao | Weizhe Yuan | Shuaichen Chang | Junqi Dai | Yixin Liu | Zihuiwen Ye | Graham Neubig
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations

With the rapid development of NLP research, leaderboards have emerged as one tool to track the performance of various systems on various NLP tasks. They are effective in this goal to some extent, but generally present a rather simplistic one-dimensional view of the submitted systems, communicated only through holistic accuracy numbers. In this paper, we present a new conceptualization and implementation of NLP evaluation: the ExplainaBoard, which in addition to inheriting the functionality of the standard leaderboard, also allows researchers to (i) diagnose strengths and weaknesses of a single system (e.g. what is the best-performing system bad at?) (ii) interpret relationships between multiple systems. (e.g. where does system A outperform system B? What if we combine systems A, B and C?) and (iii) examine prediction results closely (e.g. what are common errors made by multiple systems or in what contexts do particular errors occur?). So far, ExplainaBoard covers more than 400 systems, 50 datasets, 40 languages, and 12 tasks. We not only released an online platform at the website but also make our evaluation tool an API with MIT Licence at Github and PyPi that allows users to conveniently assess their models offline. We additionally release all output files from systems that we have run or collected to motivate “output-driven” research in the future.

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Reducing Confusion in Active Learning for Part-Of-Speech Tagging
Aditi Chaudhary | Antonios Anastasopoulos | Zaid Sheikh | Graham Neubig
Transactions of the Association for Computational Linguistics, Volume 9

Active learning (AL) uses a data selection algorithm to select useful training samples to minimize annotation cost. This is now an essential tool for building low-resource syntactic analyzers such as part-of-speech (POS) taggers. Existing AL heuristics are generally designed on the principle of selecting uncertain yet representative training instances, where annotating these instances may reduce a large number of errors. However, in an empirical study across six typologically diverse languages (German, Swedish, Galician, North Sami, Persian, and Ukrainian), we found the surprising result that even in an oracle scenario where we know the true uncertainty of predictions, these current heuristics are far from optimal. Based on this analysis, we pose the problem of AL as selecting instances that maximally reduce the confusion between particular pairs of output tags. Extensive experimentation on the aforementioned languages shows that our proposed AL strategy outperforms other AL strategies by a significant margin. We also present auxiliary results demonstrating the importance of proper calibration of models, which we ensure through cross-view training, and analysis demonstrating how our proposed strategy selects examples that more closely follow the oracle data distribution. The code is publicly released here.1

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WikiAsp: A Dataset for Multi-domain Aspect-based Summarization
Hiroaki Hayashi | Prashant Budania | Peng Wang | Chris Ackerson | Raj Neervannan | Graham Neubig
Transactions of the Association for Computational Linguistics, Volume 9

Aspect-based summarization is the task of generating focused summaries based on specific points of interest. Such summaries aid efficient analysis of text, such as quickly understanding reviews or opinions from different angles. However, due to large differences in the type of aspects for different domains (e.g., sentiment, product features), the development of previous models has tended to be domain-specific. In this paper, we propose WikiAsp,1 a large-scale dataset for multi-domain aspect- based summarization that attempts to spur research in the direction of open-domain aspect-based summarization. Specifically, we build the dataset using Wikipedia articles from 20 different domains, using the section titles and boundaries of each article as a proxy for aspect annotation. We propose several straightforward baseline models for this task and conduct experiments on the dataset. Results highlight key challenges that existing summarization models face in this setting, such as proper pronoun handling of quoted sources and consistent explanation of time-sensitive events.

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How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering
Zhengbao Jiang | Jun Araki | Haibo Ding | Graham Neubig
Transactions of the Association for Computational Linguistics, Volume 9

Recent works have shown that language models (LM) capture different types of knowledge regarding facts or common sense. However, because no model is perfect, they still fail to provide appropriate answers in many cases. In this paper, we ask the question, “How can we know when language models know, with confidence, the answer to a particular query?” We examine this question from the point of view of calibration, the property of a probabilistic model’s predicted probabilities actually being well correlated with the probabilities of correctness. We examine three strong generative models—T5, BART, and GPT-2—and study whether their probabilities on QA tasks are well calibrated, finding the answer is a relatively emphatic no. We then examine methods to calibrate such models to make their confidence scores correlate better with the likelihood of correctness through fine-tuning, post-hoc probability modification, or adjustment of the predicted outputs or inputs. Experiments on a diverse range of datasets demonstrate the effectiveness of our methods. We also perform analysis to study the strengths and limitations of these methods, shedding light on further improvements that may be made in methods for calibrating LMs. We have released the code at https://github.com/jzbjyb/lm-calibration.

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MasakhaNER: Named Entity Recognition for African Languages
David Ifeoluwa Adelani | Jade Abbott | Graham Neubig | Daniel D’souza | Julia Kreutzer | Constantine Lignos | Chester Palen-Michel | Happy Buzaaba | Shruti Rijhwani | Sebastian Ruder | Stephen Mayhew | Israel Abebe Azime | Shamsuddeen H. Muhammad | Chris Chinenye Emezue | Joyce Nakatumba-Nabende | Perez Ogayo | Aremu Anuoluwapo | Catherine Gitau | Derguene Mbaye | Jesujoba Alabi | Seid Muhie Yimam | Tajuddeen Rabiu Gwadabe | Ignatius Ezeani | Rubungo Andre Niyongabo | Jonathan Mukiibi | Verrah Otiende | Iroro Orife | Davis David | Samba Ngom | Tosin Adewumi | Paul Rayson | Mofetoluwa Adeyemi | Gerald Muriuki | Emmanuel Anebi | Chiamaka Chukwuneke | Nkiruka Odu | Eric Peter Wairagala | Samuel Oyerinde | Clemencia Siro | Tobius Saul Bateesa | Temilola Oloyede | Yvonne Wambui | Victor Akinode | Deborah Nabagereka | Maurice Katusiime | Ayodele Awokoya | Mouhamadane MBOUP | Dibora Gebreyohannes | Henok Tilaye | Kelechi Nwaike | Degaga Wolde | Abdoulaye Faye | Blessing Sibanda | Orevaoghene Ahia | Bonaventure F. P. Dossou | Kelechi Ogueji | Thierno Ibrahima DIOP | Abdoulaye Diallo | Adewale Akinfaderin | Tendai Marengereke | Salomey Osei
Transactions of the Association for Computational Linguistics, Volume 9

We take a step towards addressing the under- representation of the African continent in NLP research by bringing together different stakeholders to create the first large, publicly available, high-quality dataset for named entity recognition (NER) in ten African languages. We detail the characteristics of these languages to help researchers and practitioners better understand the challenges they pose for NER tasks. We analyze our datasets and conduct an extensive empirical evaluation of state- of-the-art methods across both supervised and transfer learning settings. Finally, we release the data, code, and models to inspire future research on African NLP.1

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Lexically Aware Semi-Supervised Learning for OCR Post-Correction
Shruti Rijhwani | Daisy Rosenblum | Antonios Anastasopoulos | Graham Neubig
Transactions of the Association for Computational Linguistics, Volume 9

Much of the existing linguistic data in many languages of the world is locked away in non- digitized books and documents. Optical character recognition (OCR) can be used to produce digitized text, and previous work has demonstrated the utility of neural post-correction methods that improve the results of general- purpose OCR systems on recognition of less- well-resourced languages. However, these methods rely on manually curated post- correction data, which are relatively scarce compared to the non-annotated raw images that need to be digitized. In this paper, we present a semi-supervised learning method that makes it possible to utilize these raw images to improve performance, specifically through the use of self-training, a technique where a model is iteratively trained on its own outputs. In addition, to enforce consistency in the recognized vocabulary, we introduce a lexically aware decoding method that augments the neural post-correction model with a count-based language model constructed from the recognized texts, implemented using weighted finite-state automata (WFSA) for efficient and effective decoding. Results on four endangered languages demonstrate the utility of the proposed method, with relative error reductions of 15%–29%, where we find the combination of self-training and lexically aware decoding essential for achieving consistent improvements.1

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Data Augmentation for Sign Language Gloss Translation
Amit Moryossef | Kayo Yin | Graham Neubig | Yoav Goldberg
Proceedings of the 1st International Workshop on Automatic Translation for Signed and Spoken Languages (AT4SSL)

Sign language translation (SLT) is often decomposed into video-to-gloss recognition and gloss to-text translation, where a gloss is a sequence of transcribed spoken-language words in the order in which they are signed. We focus here on gloss-to-text translation, which we treat as a low-resource neural machine translation (NMT) problem. However, unlike traditional low resource NMT, gloss-to-text translation differs because gloss-text pairs often have a higher lexical overlap and lower syntactic overlap than pairs of spoken languages. We exploit this lexical overlap and handle syntactic divergence by proposing two rule-based heuristics that generate pseudo-parallel gloss-text pairs from monolingual spoken language text. By pre-training on this synthetic data, we improve translation from American Sign Language (ASL) to English and German Sign Language (DGS) to German by up to 3.14 and 2.20 BLEU, respectively.

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Multi-view Subword Regularization
Xinyi Wang | Sebastian Ruder | Graham Neubig
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Multilingual pretrained representations generally rely on subword segmentation algorithms to create a shared multilingual vocabulary. However, standard heuristic algorithms often lead to sub-optimal segmentation, especially for languages with limited amounts of data. In this paper, we take two major steps towards alleviating this problem. First, we demonstrate empirically that applying existing subword regularization methods (Kudo, 2018; Provilkov et al., 2020) during fine-tuning of pre-trained multilingual representations improves the effectiveness of cross-lingual transfer. Second, to take full advantage of different possible input segmentations, we propose Multi-view Subword Regularization (MVR), a method that enforces the consistency of predictors between using inputs tokenized by the standard and probabilistic segmentations. Results on the XTREME multilingual benchmark (Hu et al., 2020) show that MVR brings consistent improvements of up to 2.5 points over using standard segmentation algorithms.

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MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning
Mengzhou Xia | Guoqing Zheng | Subhabrata Mukherjee | Milad Shokouhi | Graham Neubig | Ahmed Hassan Awadallah
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

The combination of multilingual pre-trained representations and cross-lingual transfer learning is one of the most effective methods for building functional NLP systems for low-resource languages. However, for extremely low-resource languages without large-scale monolingual corpora for pre-training or sufficient annotated data for fine-tuning, transfer learning remains an understudied and challenging task. Moreover, recent work shows that multilingual representations are surprisingly disjoint across languages, bringing additional challenges for transfer onto extremely low-resource languages. In this paper, we propose MetaXL, a meta-learning based framework that learns to transform representations judiciously from auxiliary languages to a target one and brings their representation spaces closer for effective transfer. Extensive experiments on real-world low-resource languages – without access to large-scale monolingual corpora or large amounts of labeled data – for tasks like cross-lingual sentiment analysis and named entity recognition show the effectiveness of our approach. Code for MetaXL is publicly available at github.com/microsoft/MetaXL.

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Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models
Po-Yao Huang | Mandela Patrick | Junjie Hu | Graham Neubig | Florian Metze | Alexander Hauptmann
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

This paper studies zero-shot cross-lingual transfer of vision-language models. Specifically, we focus on multilingual text-to-video search and propose a Transformer-based model that learns contextual multilingual multimodal embeddings. Under a zero-shot setting, we empirically demonstrate that performance degrades significantly when we query the multilingual text-video model with non-English sentences. To address this problem, we introduce a multilingual multimodal pre-training strategy, and collect a new multilingual instructional video dataset (Multi-HowTo100M) for pre-training. Experiments on VTT show that our method significantly improves video search in non-English languages without additional annotations. Furthermore, when multilingual annotations are available, our method outperforms recent baselines by a large margin in multilingual text-to-video search on VTT and VATEX; as well as in multilingual text-to-image search on Multi30K. Our model and Multi-HowTo100M is available at http://github.com/berniebear/Multi-HT100M.

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Compositional Generalization for Neural Semantic Parsing via Span-level Supervised Attention
Pengcheng Yin | Hao Fang | Graham Neubig | Adam Pauls | Emmanouil Antonios Platanios | Yu Su | Sam Thomson | Jacob Andreas
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We describe a span-level supervised attention loss that improves compositional generalization in semantic parsers. Our approach builds on existing losses that encourage attention maps in neural sequence-to-sequence models to imitate the output of classical word alignment algorithms. Where past work has used word-level alignments, we focus on spans; borrowing ideas from phrase-based machine translation, we align subtrees in semantic parses to spans of input sentences, and encourage neural attention mechanisms to mimic these alignments. This method improves the performance of transformers, RNNs, and structured decoders on three benchmarks of compositional generalization.

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Explicit Alignment Objectives for Multilingual Bidirectional Encoders
Junjie Hu | Melvin Johnson | Orhan Firat | Aditya Siddhant | Graham Neubig
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Pre-trained cross-lingual encoders such as mBERT (Devlin et al., 2019) and XLM-R (Conneau et al., 2020) have proven impressively effective at enabling transfer-learning of NLP systems from high-resource languages to low-resource languages. This success comes despite the fact that there is no explicit objective to align the contextual embeddings of words/sentences with similar meanings across languages together in the same space. In this paper, we present a new method for learning multilingual encoders, AMBER (Aligned Multilingual Bidirectional EncodeR). AMBER is trained on additional parallel data using two explicit alignment objectives that align the multilingual representations at different granularities. We conduct experiments on zero-shot cross-lingual transfer learning for different tasks including sequence tagging, sentence retrieval and sentence classification. Experimental results on the tasks in the XTREME benchmark (Hu et al., 2020) show that AMBER obtains gains of up to 1.1 average F1 score on sequence tagging and up to 27.3 average accuracy on retrieval over the XLM-R-large model which has 3.2x the parameters of AMBER. Our code and models are available at http://github.com/junjiehu/amber.

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On Learning Text Style Transfer with Direct Rewards
Yixin Liu | Graham Neubig | John Wieting
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

In most cases, the lack of parallel corpora makes it impossible to directly train supervised models for the text style transfer task. In this paper, we explore training algorithms that instead optimize reward functions that explicitly consider different aspects of the style-transferred outputs. In particular, we leverage semantic similarity metrics originally used for fine-tuning neural machine translation models to explicitly assess the preservation of content between system outputs and input texts. We also investigate the potential weaknesses of the existing automatic metrics and propose efficient strategies of using these metrics for training. The experimental results show that our model provides significant gains in both automatic and human evaluation over strong baselines, indicating the effectiveness of our proposed methods and training strategies.

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GSum: A General Framework for Guided Neural Abstractive Summarization
Zi-Yi Dou | Pengfei Liu | Hiroaki Hayashi | Zhengbao Jiang | Graham Neubig
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Neural abstractive summarization models are flexible and can produce coherent summaries, but they are sometimes unfaithful and can be difficult to control. While previous studies attempt to provide different types of guidance to control the output and increase faithfulness, it is not clear how these strategies compare and contrast to each other. In this paper, we propose a general and extensible guided summarization framework (GSum) that can effectively take different kinds of external guidance as input, and we perform experiments across several different varieties. Experiments demonstrate that this model is effective, achieving state-of-the-art performance according to ROUGE on 4 popular summarization datasets when using highlighted sentences as guidance. In addition, we show that our guided model can generate more faithful summaries and demonstrate how different types of guidance generate qualitatively different summaries, lending a degree of controllability to the learned models.

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Dependency Induction Through the Lens of Visual Perception
Ruisi Su | Shruti Rijhwani | Hao Zhu | Junxian He | Xinyu Wang | Yonatan Bisk | Graham Neubig
Proceedings of the 25th Conference on Computational Natural Language Learning

Most previous work on grammar induction focuses on learning phrasal or dependency structure purely from text. However, because the signal provided by text alone is limited, recently introduced visually grounded syntax models make use of multimodal information leading to improved performance in constituency grammar induction. However, as compared to dependency grammars, constituency grammars do not provide a straightforward way to incorporate visual information without enforcing language-specific heuristics. In this paper, we propose an unsupervised grammar induction model that leverages word concreteness and a structural vision-based heuristic to jointly learn constituency-structure and dependency-structure grammars. Our experiments find that concreteness is a strong indicator for learning dependency grammars, improving the direct attachment score (DAS) by over 50% as compared to state-of-the-art models trained on pure text. Next, we propose an extension of our model that leverages both word concreteness and visual semantic role labels in constituency and dependency parsing. Our experiments show that the proposed extension outperforms the current state-of-the-art visually grounded models in constituency parsing even with a smaller grammar size.

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Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas
Manuel Mager | Arturo Oncevay | Annette Rios | Ivan Vladimir Meza Ruiz | Alexis Palmer | Graham Neubig | Katharina Kann
Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas

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Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas
Manuel Mager | Arturo Oncevay | Abteen Ebrahimi | John Ortega | Annette Rios | Angela Fan | Ximena Gutierrez-Vasques | Luis Chiruzzo | Gustavo Giménez-Lugo | Ricardo Ramos | Ivan Vladimir Meza Ruiz | Rolando Coto-Solano | Alexis Palmer | Elisabeth Mager-Hois | Vishrav Chaudhary | Graham Neubig | Ngoc Thang Vu | Katharina Kann
Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas

This paper presents the results of the 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas. The shared task featured two independent tracks, and participants submitted machine translation systems for up to 10 indigenous languages. Overall, 8 teams participated with a total of 214 submissions. We provided training sets consisting of data collected from various sources, as well as manually translated sentences for the development and test sets. An official baseline trained on this data was also provided. Team submissions featured a variety of architectures, including both statistical and neural models, and for the majority of languages, many teams were able to considerably improve over the baseline. The best performing systems achieved 12.97 ChrF higher than baseline, when averaged across languages.

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Word Alignment by Fine-tuning Embeddings on Parallel Corpora
Zi-Yi Dou | Graham Neubig
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Word alignment over parallel corpora has a wide variety of applications, including learning translation lexicons, cross-lingual transfer of language processing tools, and automatic evaluation or analysis of translation outputs. The great majority of past work on word alignment has worked by performing unsupervised learning on parallel text. Recently, however, other work has demonstrated that pre-trained contextualized word embeddings derived from multilingually trained language models (LMs) prove an attractive alternative, achieving competitive results on the word alignment task even in the absence of explicit training on parallel data. In this paper, we examine methods to marry the two approaches: leveraging pre-trained LMs but fine-tuning them on parallel text with objectives designed to improve alignment quality, and proposing methods to effectively extract alignments from these fine-tuned models. We perform experiments on five language pairs and demonstrate that our model can consistently outperform previous state-of-the-art models of all varieties. In addition, we demonstrate that we are able to train multilingual word aligners that can obtain robust performance on different language pairs.

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Towards More Fine-grained and Reliable NLP Performance Prediction
Zihuiwen Ye | Pengfei Liu | Jinlan Fu | Graham Neubig
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Performance prediction, the task of estimating a system’s performance without performing experiments, allows us to reduce the experimental burden caused by the combinatorial explosion of different datasets, languages, tasks, and models. In this paper, we make two contributions to improving performance prediction for NLP tasks. First, we examine performance predictors not only for holistic measures of accuracy like F1 or BLEU, but also fine-grained performance measures such as accuracy over individual classes of examples. Second, we propose methods to understand the reliability of a performance prediction model from two angles: confidence intervals and calibration. We perform an analysis of four types of NLP tasks, and both demonstrate the feasibility of fine-grained performance prediction and the necessity to perform reliability analysis for performance prediction methods in the future.

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Phrase-level Active Learning for Neural Machine Translation
Junjie Hu | Graham Neubig
Proceedings of the Sixth Conference on Machine Translation

Neural machine translation (NMT) is sensitive to domain shift. In this paper, we address this problem in an active learning setting where we can spend a given budget on translating in-domain data, and gradually fine-tune a pre-trained out-of-domain NMT model on the newly translated data. Existing active learning methods for NMT usually select sentences based on uncertainty scores, but these methods require costly translation of full sentences even when only one or two key phrases within the sentence are informative. To address this limitation, we re-examine previous work from the phrase-based machine translation (PBMT) era that selected not full sentences, but rather individual phrases. However, while incorporating these phrases into PBMT systems was relatively simple, it is less trivial for NMT systems, which need to be trained on full sequences to capture larger structural properties of sentences unique to the new domain. To overcome these hurdles, we propose to select both full sentences and individual phrases from unlabelled data in the new domain for routing to human translators. In a German-English translation task, our active learning approach achieves consistent improvements over uncertainty-based sentence selection methods, improving up to 1.2 BLEU score over strong active learning baselines.

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Detecting Hallucinated Content in Conditional Neural Sequence Generation
Chunting Zhou | Graham Neubig | Jiatao Gu | Mona Diab | Francisco Guzmán | Luke Zettlemoyer | Marjan Ghazvininejad
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Efficient Test Time Adapter Ensembling for Low-resource Language Varieties
Xinyi Wang | Yulia Tsvetkov | Sebastian Ruder | Graham Neubig
Findings of the Association for Computational Linguistics: EMNLP 2021

Adapters are light-weight modules that allow parameter-efficient fine-tuning of pretrained models. Specialized language and task adapters have recently been proposed to facilitate cross-lingual transfer of multilingual pretrained models (Pfeiffer et al., 2020b). However, this approach requires training a separate language adapter for every language one wishes to support, which can be impractical for languages with limited data. An intuitive solution is to use a related language adapter for the new language variety, but we observe that this solution can lead to sub-optimal performance. In this paper, we aim to improve the robustness of language adapters to uncovered languages without training new adapters. We find that ensembling multiple existing language adapters makes the fine-tuned model significantly more robust to other language varieties not included in these adapters. Building upon this observation, we propose Entropy Minimized Ensemble of Adapters (EMEA), a method that optimizes the ensemble weights of the pretrained language adapters for each test sentence by minimizing the entropy of its predictions. Experiments on three diverse groups of language varieties show that our method leads to significant improvements on both named entity recognition and part-of-speech tagging across all languages.

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AfroMT: Pretraining Strategies and Reproducible Benchmarks for Translation of 8 African Languages
Machel Reid | Junjie Hu | Graham Neubig | Yutaka Matsuo
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Reproducible benchmarks are crucial in driving progress of machine translation research. However, existing machine translation benchmarks have been mostly limited to high-resource or well-represented languages. Despite an increasing interest in low-resource machine translation, there are no standardized reproducible benchmarks for many African languages, many of which are used by millions of speakers but have less digitized textual data. To tackle these challenges, we propose AfroMT, a standardized, clean, and reproducible machine translation benchmark for eight widely spoken African languages. We also develop a suite of analysis tools for system diagnosis taking into account the unique properties of these languages. Furthermore, we explore the newly considered case of low-resource focused pretraining and develop two novel data augmentation-based strategies, leveraging word-level alignment information and pseudo-monolingual data for pretraining multilingual sequence-to-sequence models. We demonstrate significant improvements when pretraining on 11 languages, with gains of up to 2 BLEU points over strong baselines. We also show gains of up to 12 BLEU points over cross-lingual transfer baselines in data-constrained scenarios. All code and pretrained models will be released as further steps towards larger reproducible benchmarks for African languages.

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Distributionally Robust Multilingual Machine Translation
Chunting Zhou | Daniel Levy | Xian Li | Marjan Ghazvininejad | Graham Neubig
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Multilingual neural machine translation (MNMT) learns to translate multiple language pairs with a single model, potentially improving both the accuracy and the memory-efficiency of deployed models. However, the heavy data imbalance between languages hinders the model from performing uniformly across language pairs. In this paper, we propose a new learning objective for MNMT based on distributionally robust optimization, which minimizes the worst-case expected loss over the set of language pairs. We further show how to practically optimize this objective for large translation corpora using an iterated best response scheme, which is both effective and incurs negligible additional computational cost compared to standard empirical risk minimization. We perform extensive experiments on three sets of languages from two datasets and show that our method consistently outperforms strong baseline methods in terms of average and per-language performance under both many-to-one and one-to-many translation settings.

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Efficient Nearest Neighbor Language Models
Junxian He | Graham Neubig | Taylor Berg-Kirkpatrick
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Non-parametric neural language models (NLMs) learn predictive distributions of text utilizing an external datastore, which allows them to learn through explicitly memorizing the training datapoints. While effective, these models often require retrieval from a large datastore at test time, significantly increasing the inference overhead and thus limiting the deployment of non-parametric NLMs in practical applications. In this paper, we take the recently proposed k-nearest neighbors language model as an example, exploring methods to improve its efficiency along various dimensions. Experiments on the standard WikiText-103 benchmark and domain-adaptation datasets show that our methods are able to achieve up to a 6x speed-up in inference speed while retaining comparable performance. The empirical analysis we present may provide guidelines for future research seeking to develop or deploy more efficient non-parametric NLMs.

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When is Wall a Pared and when a Muro?: Extracting Rules Governing Lexical Selection
Aditi Chaudhary | Kayo Yin | Antonios Anastasopoulos | Graham Neubig
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Learning fine-grained distinctions between vocabulary items is a key challenge in learning a new language. For example, the noun “wall” has different lexical manifestations in Spanish – “pared” refers to an indoor wall while “muro” refers to an outside wall. However, this variety of lexical distinction may not be obvious to non-native learners unless the distinction is explained in such a way. In this work, we present a method for automatically identifying fine-grained lexical distinctions, and extracting rules explaining these distinctions in a human- and machine-readable format. We confirm the quality of these extracted rules in a language learning setup for two languages, Spanish and Greek, where we use the rules to teach non-native speakers when to translate a given ambiguous word into its different possible translations.

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Evaluating the Morphosyntactic Well-formedness of Generated Texts
Adithya Pratapa | Antonios Anastasopoulos | Shruti Rijhwani | Aditi Chaudhary | David R. Mortensen | Graham Neubig | Yulia Tsvetkov
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Text generation systems are ubiquitous in natural language processing applications. However, evaluation of these systems remains a challenge, especially in multilingual settings. In this paper, we propose L’AMBRE – a metric to evaluate the morphosyntactic well-formedness of text using its dependency parse and morphosyntactic rules of the language. We present a way to automatically extract various rules governing morphosyntax directly from dependency treebanks. To tackle the noisy outputs from text generation systems, we propose a simple methodology to train robust parsers. We show the effectiveness of our metric on the task of machine translation through a diachronic study of systems translating into morphologically-rich languages.

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XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation
Sebastian Ruder | Noah Constant | Jan Botha | Aditya Siddhant | Orhan Firat | Jinlan Fu | Pengfei Liu | Junjie Hu | Dan Garrette | Graham Neubig | Melvin Johnson
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Machine learning has brought striking advances in multilingual natural language processing capabilities over the past year. For example, the latest techniques have improved the state-of-the-art performance on the XTREME multilingual benchmark by more than 13 points. While a sizeable gap to human-level performance remains, improvements have been easier to achieve in some tasks than in others. This paper analyzes the current state of cross-lingual transfer learning and summarizes some lessons learned. In order to catalyze meaningful progress, we extend XTREME to XTREME-R, which consists of an improved set of ten natural language understanding tasks, including challenging language-agnostic retrieval tasks, and covers 50 typologically diverse languages. In addition, we provide a massively multilingual diagnostic suite and fine-grained multi-dataset evaluation capabilities through an interactive public leaderboard to gain a better understanding of such models.

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Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)
Royi Lachmy | Ziyu Yao | Greg Durrett | Milos Gligoric | Junyi Jessy Li | Ray Mooney | Graham Neubig | Yu Su | Huan Sun | Reut Tsarfaty
Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)

2020

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Automatic Interlinear Glossing for Under-Resourced Languages Leveraging Translations
Xingyuan Zhao | Satoru Ozaki | Antonios Anastasopoulos | Graham Neubig | Lori Levin
Proceedings of the 28th International Conference on Computational Linguistics

Interlinear Glossed Text (IGT) is a widely used format for encoding linguistic information in language documentation projects and scholarly papers. Manual production of IGT takes time and requires linguistic expertise. We attempt to address this issue by creating automatic glossing models, using modern multi-source neural models that additionally leverage easy-to-collect translations. We further explore cross-lingual transfer and a simple output length control mechanism, further refining our models. Evaluated on three challenging low-resource scenarios, our approach significantly outperforms a recent, state-of-the-art baseline, particularly improving on overall accuracy as well as lemma and tag recall.

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Endangered Languages meet Modern NLP
Antonios Anastasopoulos | Christopher Cox | Graham Neubig | Hilaria Cruz
Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts

This tutorial will focus on NLP for endangered languages documentation and revitalization. First, we will acquaint the attendees with the process and the challenges of language documentation, showing how the needs of the language communities and the documentary linguists map to specific NLP tasks. We will then present the state-of-the-art in NLP applied in this particularly challenging setting (extremely low-resource datasets, noisy transcriptions, limited annotations, non-standard orthographies). In doing so, we will also analyze the challenges of working in this domain and expand on both the capabilities and the limitations of current NLP approaches. Our ultimate goal is to motivate more NLP practitioners to work towards this very important direction, and also provide them with the tools and understanding of the limitations/challenges, both of which are needed in order to have an impact.

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Proceedings of the Fourth Workshop on Neural Generation and Translation
Alexandra Birch | Andrew Finch | Hiroaki Hayashi | Kenneth Heafield | Marcin Junczys-Dowmunt | Ioannis Konstas | Xian Li | Graham Neubig | Yusuke Oda
Proceedings of the Fourth Workshop on Neural Generation and Translation

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Findings of the Fourth Workshop on Neural Generation and Translation
Kenneth Heafield | Hiroaki Hayashi | Yusuke Oda | Ioannis Konstas | Andrew Finch | Graham Neubig | Xian Li | Alexandra Birch
Proceedings of the Fourth Workshop on Neural Generation and Translation

We describe the finding of the Fourth Workshop on Neural Generation and Translation, held in concert with the annual conference of the Association for Computational Linguistics (ACL 2020). First, we summarize the research trends of papers presented in the proceedings. Second, we describe the results of the three shared tasks 1) efficient neural machine translation (NMT) where participants were tasked with creating NMT systems that are both accurate and efficient, and 2) document-level generation and translation (DGT) where participants were tasked with developing systems that generate summaries from structured data, potentially with assistance from text in another language and 3) STAPLE task: creation of as many possible translations of a given input text. This last shared task was organised by Duolingo.

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Findings of the WMT 2020 Shared Task on Machine Translation Robustness
Lucia Specia | Zhenhao Li | Juan Pino | Vishrav Chaudhary | Francisco Guzmán | Graham Neubig | Nadir Durrani | Yonatan Belinkov | Philipp Koehn | Hassan Sajjad | Paul Michel | Xian Li
Proceedings of the Fifth Conference on Machine Translation

We report the findings of the second edition of the shared task on improving robustness in Machine Translation (MT). The task aims to test current machine translation systems in their ability to handle challenges facing MT models to be deployed in the real world, including domain diversity and non-standard texts common in user generated content, especially in social media. We cover two language pairs – English-German and English-Japanese and provide test sets in zero-shot and few-shot variants. Participating systems are evaluated both automatically and manually, with an additional human evaluation for ”catastrophic errors”. We received 59 submissions by 11 participating teams from a variety of types of institutions.

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Transliteration for Cross-Lingual Morphological Inflection
Nikitha Murikinati | Antonios Anastasopoulos | Graham Neubig
Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

Cross-lingual transfer between typologically related languages has been proven successful for the task of morphological inflection. However, if the languages do not share the same script, current methods yield more modest improvements. We explore the use of transliteration between related languages, as well as grapheme-to-phoneme conversion, as data preprocessing methods in order to alleviate this issue. We experimented with several diverse language pairs, finding that in most cases transliterating the transfer language data into the target one leads to accuracy improvements, even up to 9 percentage points. Converting both languages into a shared space like the International Phonetic Alphabet or the Latin alphabet is also beneficial, leading to improvements of up to 16 percentage points.

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AlloVera: A Multilingual Allophone Database
David R. Mortensen | Xinjian Li | Patrick Littell | Alexis Michaud | Shruti Rijhwani | Antonios Anastasopoulos | Alan W Black | Florian Metze | Graham Neubig
Proceedings of the Twelfth Language Resources and Evaluation Conference

We introduce a new resource, AlloVera, which provides mappings from 218 allophones to phonemes for 14 languages. Phonemes are contrastive phonological units, and allophones are their various concrete realizations, which are predictable from phonological context. While phonemic representations are language specific, phonetic representations (stated in terms of (allo)phones) are much closer to a universal (language-independent) transcription. AlloVera allows the training of speech recognition models that output phonetic transcriptions in the International Phonetic Alphabet (IPA), regardless of the input language. We show that a “universal” allophone model, Allosaurus, built with AlloVera, outperforms “universal” phonemic models and language-specific models on a speech-transcription task. We explore the implications of this technology (and related technologies) for the documentation of endangered and minority languages. We further explore other applications for which AlloVera will be suitable as it grows, including phonological typology.

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Politeness Transfer: A Tag and Generate Approach
Aman Madaan | Amrith Setlur | Tanmay Parekh | Barnabas Poczos | Graham Neubig | Yiming Yang | Ruslan Salakhutdinov | Alan W Black | Shrimai Prabhumoye
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

This paper introduces a new task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning. We also provide a dataset of more than 1.39 instances automatically labeled for politeness to encourage benchmark evaluations on this new task. We design a tag and generate pipeline that identifies stylistic attributes and subsequently generates a sentence in the target style while preserving most of the source content. For politeness as well as five other transfer tasks, our model outperforms the state-of-the-art methods on automatic metrics for content preservation, with a comparable or better performance on style transfer accuracy. Additionally, our model surpasses existing methods on human evaluations for grammaticality, meaning preservation and transfer accuracy across all the six style transfer tasks. The data and code is located at https://github.com/tag-and-generate.

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Generalizing Natural Language Analysis through Span-relation Representations
Zhengbao Jiang | Wei Xu | Jun Araki | Graham Neubig
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Natural language processing covers a wide variety of tasks predicting syntax, semantics, and information content, and usually each type of output is generated with specially designed architectures. In this paper, we provide the simple insight that a great variety of tasks can be represented in a single unified format consisting of labeling spans and relations between spans, thus a single task-independent model can be used across different tasks. We perform extensive experiments to test this insight on 10 disparate tasks spanning dependency parsing (syntax), semantic role labeling (semantics), relation extraction (information content), aspect based sentiment analysis (sentiment), and many others, achieving performance comparable to state-of-the-art specialized models. We further demonstrate benefits of multi-task learning, and also show that the proposed method makes it easy to analyze differences and similarities in how the model handles different tasks. Finally, we convert these datasets into a unified format to build a benchmark, which provides a holistic testbed for evaluating future models for generalized natural language analysis.

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Weight Poisoning Attacks on Pretrained Models
Keita Kurita | Paul Michel | Graham Neubig
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Recently, NLP has seen a surge in the usage of large pre-trained models. Users download weights of models pre-trained on large datasets, then fine-tune the weights on a task of their choice. This raises the question of whether downloading untrusted pre-trained weights can pose a security threat. In this paper, we show that it is possible to construct “weight poisoning” attacks where pre-trained weights are injected with vulnerabilities that expose “backdoors” after fine-tuning, enabling the attacker to manipulate the model prediction simply by injecting an arbitrary keyword. We show that by applying a regularization method which we call RIPPLe and an initialization procedure we call Embedding Surgery, such attacks are possible even with limited knowledge of the dataset and fine-tuning procedure. Our experiments on sentiment classification, toxicity detection, and spam detection show that this attack is widely applicable and poses a serious threat. Finally, we outline practical defenses against such attacks.

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Learning to Deceive with Attention-Based Explanations
Danish Pruthi | Mansi Gupta | Bhuwan Dhingra | Graham Neubig | Zachary C. Lipton
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Attention mechanisms are ubiquitous components in neural architectures applied to natural language processing. In addition to yielding gains in predictive accuracy, attention weights are often claimed to confer interpretability, purportedly useful both for providing insights to practitioners and for explaining why a model makes its decisions to stakeholders. We call the latter use of attention mechanisms into question by demonstrating a simple method for training models to produce deceptive attention masks. Our method diminishes the total weight assigned to designated impermissible tokens, even when the models can be shown to nevertheless rely on these features to drive predictions. Across multiple models and tasks, our approach manipulates attention weights while paying surprisingly little cost in accuracy. Through a human study, we show that our manipulated attention-based explanations deceive people into thinking that predictions from a model biased against gender minorities do not rely on the gender. Consequently, our results cast doubt on attention’s reliability as a tool for auditing algorithms in the context of fairness and accountability.

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Incorporating External Knowledge through Pre-training for Natural Language to Code Generation
Frank F. Xu | Zhengbao Jiang | Pengcheng Yin | Bogdan Vasilescu | Graham Neubig
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Open-domain code generation aims to generate code in a general-purpose programming language (such as Python) from natural language (NL) intents. Motivated by the intuition that developers usually retrieve resources on the web when writing code, we explore the effectiveness of incorporating two varieties of external knowledge into NL-to-code generation: automatically mined NL-code pairs from the online programming QA forum StackOverflow and programming language API documentation. Our evaluations show that combining the two sources with data augmentation and retrieval-based data re-sampling improves the current state-of-the-art by up to 2.2% absolute BLEU score on the code generation testbed CoNaLa. The code and resources are available at https://github.com/neulab/external-knowledge-codegen.

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Soft Gazetteers for Low-Resource Named Entity Recognition
Shruti Rijhwani | Shuyan Zhou | Graham Neubig | Jaime Carbonell
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Traditional named entity recognition models use gazetteers (lists of entities) as features to improve performance. Although modern neural network models do not require such hand-crafted features for strong performance, recent work has demonstrated their utility for named entity recognition on English data. However, designing such features for low-resource languages is challenging, because exhaustive entity gazetteers do not exist in these languages. To address this problem, we propose a method of “soft gazetteers” that incorporates ubiquitously available information from English knowledge bases, such as Wikipedia, into neural named entity recognition models through cross-lingual entity linking. Our experiments on four low-resource languages show an average improvement of 4 points in F1 score.

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TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
Pengcheng Yin | Graham Neubig | Wen-tau Yih | Sebastian Riedel
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Recent years have witnessed the burgeoning of pretrained language models (LMs) for text-based natural language (NL) understanding tasks. Such models are typically trained on free-form NL text, hence may not be suitable for tasks like semantic parsing over structured data, which require reasoning over both free-form NL questions and structured tabular data (e.g., database tables). In this paper we present TaBERT, a pretrained LM that jointly learns representations for NL sentences and (semi-)structured tables. TaBERT is trained on a large corpus of 26 million tables and their English contexts. In experiments, neural semantic parsers using TaBERT as feature representation layers achieve new best results on the challenging weakly-supervised semantic parsing benchmark WikiTableQuestions, while performing competitively on the text-to-SQL dataset Spider.

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Balancing Training for Multilingual Neural Machine Translation
Xinyi Wang | Yulia Tsvetkov | Graham Neubig
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

When training multilingual machine translation (MT) models that can translate to/from multiple languages, we are faced with imbalanced training sets: some languages have much more training data than others. Standard practice is to up-sample less resourced languages to increase representation, and the degree of up-sampling has a large effect on the overall performance. In this paper, we propose a method that instead automatically learns how to weight training data through a data scorer that is optimized to maximize performance on all test languages. Experiments on two sets of languages under both one-to-many and many-to-one MT settings show our method not only consistently outperforms heuristic baselines in terms of average performance, but also offers flexible control over the performance of which languages are optimized.

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Predicting Performance for Natural Language Processing Tasks
Mengzhou Xia | Antonios Anastasopoulos | Ruochen Xu | Yiming Yang | Graham Neubig
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Given the complexity of combinations of tasks, languages, and domains in natural language processing (NLP) research, it is computationally prohibitive to exhaustively test newly proposed models on each possible experimental setting. In this work, we attempt to explore the possibility of gaining plausible judgments of how well an NLP model can perform under an experimental setting, without actually training or testing the model. To do so, we build regression models to predict the evaluation score of an NLP experiment given the experimental settings as input. Experimenting on~9 different NLP tasks, we find that our predictors can produce meaningful predictions over unseen languages and different modeling architectures, outperforming reasonable baselines as well as human experts. %we represent experimental settings using an array of features. Going further, we outline how our predictor can be used to find a small subset of representative experiments that should be run in order to obtain plausible predictions for all other experimental settings.

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Should All Cross-Lingual Embeddings Speak English?
Antonios Anastasopoulos | Graham Neubig
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Most of recent work in cross-lingual word embeddings is severely Anglocentric. The vast majority of lexicon induction evaluation dictionaries are between English and another language, and the English embedding space is selected by default as the hub when learning in a multilingual setting. With this work, however, we challenge these practices. First, we show that the choice of hub language can significantly impact downstream lexicon induction zero-shot POS tagging performance. Second, we both expand a standard English-centered evaluation dictionary collection to include all language pairs using triangulation, and create new dictionaries for under-represented languages. Evaluating established methods over all these language pairs sheds light into their suitability for aligning embeddings from distant languages and presents new challenges for the field. Finally, in our analysis we identify general guidelines for strong cross-lingual embedding baselines, that extend to language pairs that do not include English.

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TICO-19: the Translation Initiative for COvid-19
Antonios Anastasopoulos | Alessandro Cattelan | Zi-Yi Dou | Marcello Federico | Christian Federmann | Dmitriy Genzel | Franscisco Guzmán | Junjie Hu | Macduff Hughes | Philipp Koehn | Rosie Lazar | Will Lewis | Graham Neubig | Mengmeng Niu | Alp Öktem | Eric Paquin | Grace Tang | Sylwia Tur
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

The COVID-19 pandemic is the worst pandemic to strike the world in over a century. Crucial to stemming the tide of the SARS-CoV-2 virus is communicating to vulnerable populations the means by which they can protect themselves. To this end, the collaborators forming the Translation Initiative for COvid-19 (TICO-19) have made test and development data available to AI and MT researchers in 35 different languages in order to foster the development of tools and resources for improving access to information about COVID-19 in these languages. In addition to 9 high-resourced, ”pivot” languages, the team is targeting 26 lesser resourced languages, in particular languages of Africa, South Asia and South-East Asia, whose populations may be the most vulnerable to the spread of the virus. The same data is translated into all of the languages represented, meaning that testing or development can be done for any pairing of languages in the set. Further, the team is converting the test and development data into translation memories (TMXs) that can be used by localizers from and to any of the languages.

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A Summary of the First Workshop on Language Technology for Language Documentation and Revitalization
Graham Neubig | Shruti Rijhwani | Alexis Palmer | Jordan MacKenzie | Hilaria Cruz | Xinjian Li | Matthew Lee | Aditi Chaudhary | Luke Gessler | Steven Abney | Shirley Anugrah Hayati | Antonios Anastasopoulos | Olga Zamaraeva | Emily Prud’hommeaux | Jennette Child | Sara Child | Rebecca Knowles | Sarah Moeller | Jeffrey Micher | Yiyuan Li | Sydney Zink | Mengzhou Xia | Roshan Sharma | Patrick Littell
Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)

Despite recent advances in natural language processing and other language technology, the application of such technology to language documentation and conservation has been limited. In August 2019, a workshop was held at Carnegie Mellon University in Pittsburgh, PA, USA to attempt to bring together language community members, documentary linguists, and technologists to discuss how to bridge this gap and create prototypes of novel and practical language revitalization technologies. The workshop focused on developing technologies to aid language documentation and revitalization in four areas: 1) spoken language (speech transcription, phone to orthography decoding, text-to-speech and text-speech forced alignment), 2) dictionary extraction and management, 3) search tools for corpora, and 4) social media (language learning bots and social media analysis). This paper reports the results of this workshop, including issues discussed, and various conceived and implemented technologies for nine languages: Arapaho, Cayuga, Inuktitut, Irish Gaelic, Kidaw’ida, Kwak’wala, Ojibwe, San Juan Quiahije Chatino, and Seneca.

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Project MAIA: Multilingual AI Agent Assistant
André F. T. Martins | Joao Graca | Paulo Dimas | Helena Moniz | Graham Neubig
Proceedings of the 22nd Annual Conference of the European Association for Machine Translation

This paper presents the Multilingual Artificial Intelligence Agent Assistant (MAIA), a project led by Unbabel with the collaboration of CMU, INESC-ID and IT Lisbon. MAIA will employ cutting-edge machine learning and natural language processing technologies to build multilingual AI agent assistants, eliminating language barriers. MAIA’s translation layer will empower human agents to provide customer support in real-time, in any language, with human quality.

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A Benchmark for Structured Procedural Knowledge Extraction from Cooking Videos
Frank F. Xu | Lei Ji | Botian Shi | Junyi Du | Graham Neubig | Yonatan Bisk | Nan Duan
Proceedings of the First International Workshop on Natural Language Processing Beyond Text

Watching instructional videos are often used to learn about procedures. Video captioning is one way of automatically collecting such knowledge. However, it provides only an indirect, overall evaluation of multimodal models with no finer-grained quantitative measure of what they have learned. We propose instead, a benchmark of structured procedural knowledge extracted from cooking videos. This work is complementary to existing tasks, but requires models to produce interpretable structured knowledge in the form of verb-argument tuples. Our manually annotated open-vocabulary resource includes 356 instructional cooking videos and 15,523 video clip/sentence-level annotations. Our analysis shows that the proposed task is challenging and standard modeling approaches like unsupervised segmentation, semantic role labeling, and visual action detection perform poorly when forced to predict every action of a procedure in a structured form.

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Improving Candidate Generation for Low-resource Cross-lingual Entity Linking
Shuyan Zhou | Shruti Rijhwani | John Wieting | Jaime Carbonell | Graham Neubig
Transactions of the Association for Computational Linguistics, Volume 8

Cross-lingual entity linking (XEL) is the task of finding referents in a target-language knowledge base (KB) for mentions extracted from source-language texts. The first step of (X)EL is candidate generation, which retrieves a list of plausible candidate entities from the target-language KB for each mention. Approaches based on resources from Wikipedia have proven successful in the realm of relatively high-resource languages, but these do not extend well to low-resource languages with few, if any, Wikipedia pages. Recently, transfer learning methods have been shown to reduce the demand for resources in the low-resource languages by utilizing resources in closely related languages, but the performance still lags far behind their high-resource counterparts. In this paper, we first assess the problems faced by current entity candidate generation methods for low-resource XEL, then propose three improvements that (1) reduce the disconnect between entity mentions and KB entries, and (2) improve the robustness of the model to low-resource scenarios. The methods are simple, but effective: We experiment with our approach on seven XEL datasets and find that they yield an average gain of 16.9% in Top-30 gold candidate recall, compared with state-of-the-art baselines. Our improved model also yields an average gain of 7.9% in in-KB accuracy of end-to-end XEL.1

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How Can We Know What Language Models Know?
Zhengbao Jiang | Frank F. Xu | Jun Araki | Graham Neubig
Transactions of the Association for Computational Linguistics, Volume 8

Recent work has presented intriguing results examining the knowledge contained in language models (LMs) by having the LM fill in the blanks of prompts such as “Obama is a __ by profession”. These prompts are usually manually created, and quite possibly sub-optimal; another prompt such as “Obama worked as a __ ” may result in more accurately predicting the correct profession. Because of this, given an inappropriate prompt, we might fail to retrieve facts that the LM does know, and thus any given prompt only provides a lower bound estimate of the knowledge contained in an LM. In this paper, we attempt to more accurately estimate the knowledge contained in LMs by automatically discovering better prompts to use in this querying process. Specifically, we propose mining-based and paraphrasing-based methods to automatically generate high-quality and diverse prompts, as well as ensemble methods to combine answers from different prompts. Extensive experiments on the LAMA benchmark for extracting relational knowledge from LMs demonstrate that our methods can improve accuracy from 31.1% to 39.6%, providing a tighter lower bound on what LMs know. We have released the code and the resulting LM Prompt And Query Archive (LPAQA) at https://github.com/jzbjyb/LPAQA.

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The Return of Lexical Dependencies: Neural Lexicalized PCFGs
Hao Zhu | Yonatan Bisk | Graham Neubig
Transactions of the Association for Computational Linguistics, Volume 8

In this paper we demonstrate that context free grammar (CFG) based methods for grammar induction benefit from modeling lexical dependencies. This contrasts to the most popular current methods for grammar induction, which focus on discovering either constituents or dependencies. Previous approaches to marry these two disparate syntactic formalisms (e.g., lexicalized PCFGs) have been plagued by sparsity, making them unsuitable for unsupervised grammar induction. However, in this work, we present novel neural models of lexicalized PCFGs that allow us to overcome sparsity problems and effectively induce both constituents and dependencies within a single model. Experiments demonstrate that this unified framework results in stronger results on both representations than achieved when modeling either formalism alone.1

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Improving Target-side Lexical Transfer in Multilingual Neural Machine Translation
Luyu Gao | Xinyi Wang | Graham Neubig
Findings of the Association for Computational Linguistics: EMNLP 2020

To improve the performance of Neural Machine Translation (NMT) for low-resource languages (LRL), one effective strategy is to leverage parallel data from a related high-resource language (HRL). However, multilingual data has been found more beneficial for NMT models that translate from the LRL to a target language than the ones that translate into the LRLs. In this paper, we aim to improve the effectiveness of multilingual transfer for NMT models that translate into the LRL, by designing a better decoder word embedding. Extending upon a general-purpose multilingual encoding method Soft Decoupled Encoding (Wang et al., 2019), we propose DecSDE, an efficient character n-gram based embedding specifically designed for the NMT decoder. Our experiments show that DecSDE leads to consistent gains of up to 1.8 BLEU on translation from English to four different languages.

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Weakly- and Semi-supervised Evidence Extraction
Danish Pruthi | Bhuwan Dhingra | Graham Neubig | Zachary C. Lipton
Findings of the Association for Computational Linguistics: EMNLP 2020

For many prediction tasks, stakeholders desire not only predictions but also supporting evidence that a human can use to verify its correctness. However, in practice, evidence annotations may only be available for a minority of training examples (if available at all). In this paper, we propose new methods to combine few evidence annotations (strong semi-supervision) with abundant document-level labels (weak supervision) for the task of evidence extraction. Evaluating on two classification tasks that feature evidence annotations, we find that our methods outperform baselines adapted from the interpretability literature to our task. Our approach yields gains with as few as hundred evidence annotations.

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A Bilingual Generative Transformer for Semantic Sentence Embedding
John Wieting | Graham Neubig | Taylor Berg-Kirkpatrick
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Semantic sentence embedding models encode natural language sentences into vectors, such that closeness in embedding space indicates closeness in the semantics between the sentences. Bilingual data offers a useful signal for learning such embeddings: properties shared by both sentences in a translation pair are likely semantic, while divergent properties are likely stylistic or language-specific. We propose a deep latent variable model that attempts to perform source separation on parallel sentences, isolating what they have in common in a latent semantic vector, and explaining what is left over with language-specific latent vectors. Our proposed approach differs from past work on semantic sentence encoding in two ways. First, by using a variational probabilistic framework, we introduce priors that encourage source separation, and can use our model’s posterior to predict sentence embeddings for monolingual data at test time. Second, we use high-capacity transformers as both data generating distributions and inference networks – contrasting with most past work on sentence embeddings. In experiments, our approach substantially outperforms the state-of-the-art on a standard suite of unsupervised semantic similarity evaluations. Further, we demonstrate that our approach yields the largest gains on more difficult subsets of these evaluations where simple word overlap is not a good indicator of similarity.

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Automatic Extraction of Rules Governing Morphological Agreement
Aditi Chaudhary | Antonios Anastasopoulos | Adithya Pratapa | David R. Mortensen | Zaid Sheikh | Yulia Tsvetkov | Graham Neubig
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Creating a descriptive grammar of a language is an indispensable step for language documentation and preservation. However, at the same time it is a tedious, time-consuming task. In this paper, we take steps towards automating this process by devising an automated framework for extracting a first-pass grammatical specification from raw text in a concise, human- and machine-readable format. We focus on extracting rules describing agreement, a morphosyntactic phenomenon at the core of the grammars of many of the world’s languages. We apply our framework to all languages included in the Universal Dependencies project, with promising results. Using cross-lingual transfer, even with no expert annotations in the language of interest, our framework extracts a grammatical specification which is nearly equivalent to those created with large amounts of gold-standard annotated data. We confirm this finding with human expert evaluations of the rules that our framework produces, which have an average accuracy of 78%. We release an interface demonstrating the extracted rules at https://neulab.github.io/lase/

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Dynamic Data Selection and Weighting for Iterative Back-Translation
Zi-Yi Dou | Antonios Anastasopoulos | Graham Neubig
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Back-translation has proven to be an effective method to utilize monolingual data in neural machine translation (NMT), and iteratively conducting back-translation can further improve the model performance. Selecting which monolingual data to back-translate is crucial, as we require that the resulting synthetic data are of high quality and reflect the target domain. To achieve these two goals, data selection and weighting strategies have been proposed, with a common practice being to select samples close to the target domain but also dissimilar to the average general-domain text. In this paper, we provide insights into this commonly used approach and generalize it to a dynamic curriculum learning strategy, which is applied to iterative back-translation models. In addition, we propose weighting strategies based on both the current quality of the sentence and its improvement over the previous iteration. We evaluate our models on domain adaptation, low-resource, and high-resource MT settings and on two language pairs. Experimental results demonstrate that our methods achieve improvements of up to 1.8 BLEU points over competitive baselines.

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OCR Post Correction for Endangered Language Texts
Shruti Rijhwani | Antonios Anastasopoulos | Graham Neubig
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

There is little to no data available to build natural language processing models for most endangered languages. However, textual data in these languages often exists in formats that are not machine-readable, such as paper books and scanned images. In this work, we address the task of extracting text from these resources. We create a benchmark dataset of transcriptions for scanned books in three critically endangered languages and present a systematic analysis of how general-purpose OCR tools are not robust to the data-scarce setting of endangered languages. We develop an OCR post-correction method tailored to ease training in this data-scarce setting, reducing the recognition error rate by 34% on average across the three languages.

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X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models
Zhengbao Jiang | Antonios Anastasopoulos | Jun Araki | Haibo Ding | Graham Neubig
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Language models (LMs) have proven surprisingly successful at capturing factual knowledge by completing cloze-style fill-in-the-blank questions such as “Punta Cana is located in _.” However, while knowledge is both written and queried in many languages, studies on LMs’ factual representation ability have almost invariably been performed on English. To assess factual knowledge retrieval in LMs in different languages, we create a multilingual benchmark of cloze-style probes for typologically diverse languages. To properly handle language variations, we expand probing methods from single- to multi-word entities, and develop several decoding algorithms to generate multi-token predictions. Extensive experimental results provide insights about how well (or poorly) current state-of-the-art LMs perform at this task in languages with more or fewer available resources. We further propose a code-switching-based method to improve the ability of multilingual LMs to access knowledge, and verify its effectiveness on several benchmark languages. Benchmark data and code have be released at https://x-factr.github.io.

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Interpretable Multi-dataset Evaluation for Named Entity Recognition
Jinlan Fu | Pengfei Liu | Graham Neubig
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

With the proliferation of models for natural language processing tasks, it is even harder to understand the differences between models and their relative merits. Simply looking at differences between holistic metrics such as accuracy, BLEU, or F1 does not tell us why or how particular methods perform differently and how diverse datasets influence the model design choices. In this paper, we present a general methodology for interpretable evaluation for the named entity recognition (NER) task. The proposed evaluation method enables us to interpret the differences in models and datasets, as well as the interplay between them, identifying the strengths and weaknesses of current systems. By making our analysis tool available, we make it easy for future researchers to run similar analyses and drive progress in this area: https://github.com/neulab/InterpretEval

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Re-evaluating Evaluation in Text Summarization
Manik Bhandari | Pranav Narayan Gour | Atabak Ashfaq | Pengfei Liu | Graham Neubig
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Automated evaluation metrics as a stand-in for manual evaluation are an essential part of the development of text-generation tasks such as text summarization. However, while the field has progressed, our standard metrics have not – for nearly 20 years ROUGE has been the standard evaluation in most summarization papers. In this paper, we make an attempt to re-evaluate the evaluation method for text summarization: assessing the reliability of automatic metrics using top-scoring system outputs, both abstractive and extractive, on recently popular datasets for both system-level and summary-level evaluation settings. We find that conclusions about evaluation metrics on older datasets do not necessarily hold on modern datasets and systems. We release a dataset of human judgments that are collected from 25 top-scoring neural summarization systems (14 abstractive and 11 extractive).

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NeuSpell: A Neural Spelling Correction Toolkit
Sai Muralidhar Jayanthi | Danish Pruthi | Graham Neubig
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We introduce NeuSpell, an open-source toolkit for spelling correction in English. Our toolkit comprises ten different models, and benchmarks them on naturally occurring misspellings from multiple sources. We find that many systems do not adequately leverage the context around the misspelt token. To remedy this, (i) we train neural models using spelling errors in context, synthetically constructed by reverse engineering isolated misspellings; and (ii) use richer representations of the context. By training on our synthetic examples, correction rates improve by 9% (absolute) compared to the case when models are trained on randomly sampled character perturbations. Using richer contextual representations boosts the correction rate by another 3%. Our toolkit enables practitioners to use our proposed and existing spelling correction systems, both via a simple unified command line, as well as a web interface. Among many potential applications, we demonstrate the utility of our spell-checkers in combating adversarial misspellings. The toolkit can be accessed at neuspell.github.io.

2019

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Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces
Barun Patra | Joel Ruben Antony Moniz | Sarthak Garg | Matthew R. Gormley | Graham Neubig
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Recent work on bilingual lexicon induction (BLI) has frequently depended either on aligned bilingual lexicons or on distribution matching, often with an assumption about the isometry of the two spaces. We propose a technique to quantitatively estimate this assumption of the isometry between two embedding spaces and empirically show that this assumption weakens as the languages in question become increasingly etymologically distant. We then propose Bilingual Lexicon Induction with Semi-Supervision (BLISS) — a semi-supervised approach that relaxes the isometric assumption while leveraging both limited aligned bilingual lexicons and a larger set of unaligned word embeddings, as well as a novel hubness filtering technique. Our proposed method obtains state of the art results on 15 of 18 language pairs on the MUSE dataset, and does particularly well when the embedding spaces don’t appear to be isometric. In addition, we also show that adding supervision stabilizes the learning procedure, and is effective even with minimal supervision.

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Self-Attentional Models for Lattice Inputs
Matthias Sperber | Graham Neubig | Ngoc-Quan Pham | Alex Waibel
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Lattices are an efficient and effective method to encode ambiguity of upstream systems in natural language processing tasks, for example to compactly capture multiple speech recognition hypotheses, or to represent multiple linguistic analyses. Previous work has extended recurrent neural networks to model lattice inputs and achieved improvements in various tasks, but these models suffer from very slow computation speeds. This paper extends the recently proposed paradigm of self-attention to handle lattice inputs. Self-attention is a sequence modeling technique that relates inputs to one another by computing pairwise similarities and has gained popularity for both its strong results and its computational efficiency. To extend such models to handle lattices, we introduce probabilistic reachability masks that incorporate lattice structure into the model and support lattice scores if available. We also propose a method for adapting positional embeddings to lattice structures. We apply the proposed model to a speech translation task and find that it outperforms all examined baselines while being much faster to compute than previous neural lattice models during both training and inference.

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Domain Adaptation of Neural Machine Translation by Lexicon Induction
Junjie Hu | Mengzhou Xia | Graham Neubig | Jaime Carbonell
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

It has been previously noted that neural machine translation (NMT) is very sensitive to domain shift. In this paper, we argue that this is a dual effect of the highly lexicalized nature of NMT, resulting in failure for sentences with large numbers of unknown words, and lack of supervision for domain-specific words. To remedy this problem, we propose an unsupervised adaptation method which fine-tunes a pre-trained out-of-domain NMT model using a pseudo-in-domain corpus. Specifically, we perform lexicon induction to extract an in-domain lexicon, and construct a pseudo-parallel in-domain corpus by performing word-for-word back-translation of monolingual in-domain target sentences. In five domains over twenty pairwise adaptation settings and two model architectures, our method achieves consistent improvements without using any in-domain parallel sentences, improving up to 14 BLEU over unadapted models, and up to 2 BLEU over strong back-translation baselines.

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Choosing Transfer Languages for Cross-Lingual Learning
Yu-Hsiang Lin | Chian-Yu Chen | Jean Lee | Zirui Li | Yuyan Zhang | Mengzhou Xia | Shruti Rijhwani | Junxian He | Zhisong Zhang | Xuezhe Ma | Antonios Anastasopoulos | Patrick Littell | Graham Neubig
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Cross-lingual transfer, where a high-resource transfer language is used to improve the accuracy of a low-resource task language, is now an invaluable tool for improving performance of natural language processing (NLP) on low-resource languages. However, given a particular task language, it is not clear which language to transfer from, and the standard strategy is to select languages based on ad hoc criteria, usually the intuition of the experimenter. Since a large number of features contribute to the success of cross-lingual transfer (including phylogenetic similarity, typological properties, lexical overlap, or size of available data), even the most enlightened experimenter rarely considers all these factors for the particular task at hand. In this paper, we consider this task of automatically selecting optimal transfer languages as a ranking problem, and build models that consider the aforementioned features to perform this prediction. In experiments on representative NLP tasks, we demonstrate that our model predicts good transfer languages much better than ad hoc baselines considering single features in isolation, and glean insights on what features are most informative for each different NLP tasks, which may inform future ad hoc selection even without use of our method.

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Cross-Lingual Syntactic Transfer through Unsupervised Adaptation of Invertible Projections
Junxian He | Zhisong Zhang | Taylor Berg-Kirkpatrick | Graham Neubig
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Cross-lingual transfer is an effective way to build syntactic analysis tools in low-resource languages. However, transfer is difficult when transferring to typologically distant languages, especially when neither annotated target data nor parallel corpora are available. In this paper, we focus on methods for cross-lingual transfer to distant languages and propose to learn a generative model with a structured prior that utilizes labeled source data and unlabeled target data jointly. The parameters of source model and target model are softly shared through a regularized log likelihood objective. An invertible projection is employed to learn a new interlingual latent embedding space that compensates for imperfect cross-lingual word embedding input. We evaluate our method on two syntactic tasks: part-of-speech (POS) tagging and dependency parsing. On the Universal Dependency Treebanks, we use English as the only source corpus and transfer to a wide range of target languages. On the 10 languages in this dataset that are distant from English, our method yields an average of 5.2% absolute improvement on POS tagging and 8.3% absolute improvement on dependency parsing over a direct transfer method using state-of-the-art discriminative models.

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Beyond BLEU: Training Neural Machine Translation with Semantic Similarity
John Wieting | Taylor Berg-Kirkpatrick | Kevin Gimpel | Graham Neubig
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

While most neural machine translation (NMT)systems are still trained using maximum likelihood estimation, recent work has demonstrated that optimizing systems to directly improve evaluation metrics such as BLEU can significantly improve final translation accuracy. However, training with BLEU has some limitations: it doesn’t assign partial credit, it has a limited range of output values, and it can penalize semantically correct hypotheses if they differ lexically from the reference. In this paper, we introduce an alternative reward function for optimizing NMT systems that is based on recent work in semantic similarity. We evaluate on four disparate languages trans-lated to English, and find that training with our proposed metric results in better translations as evaluated by BLEU, semantic similarity, and human evaluation, and also that the optimization procedure converges faster. Analysis suggests that this is because the proposed metric is more conducive to optimization, assigning partial credit and providing more diversity in scores than BLEU

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Reranking for Neural Semantic Parsing
Pengcheng Yin | Graham Neubig
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Semantic parsing considers the task of transducing natural language (NL) utterances into machine executable meaning representations (MRs). While neural network-based semantic parsers have achieved impressive improvements over previous methods, results are still far from perfect, and cursory manual inspection can easily identify obvious problems such as lack of adequacy or coherence of the generated MRs. This paper presents a simple approach to quickly iterate and improve the performance of an existing neural semantic parser by reranking an n-best list of predicted MRs, using features that are designed to fix observed problems with baseline models. We implement our reranker in a competitive neural semantic parser and test on four semantic parsing (GEO, ATIS) and Python code generation (Django, CoNaLa) tasks, improving the strong baseline parser by up to 5.7% absolute in BLEU (CoNaLa) and 2.9% in accuracy (Django), outperforming the best published neural parser results on all four datasets.

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Simple and Effective Paraphrastic Similarity from Parallel Translations
John Wieting | Kevin Gimpel | Graham Neubig | Taylor Berg-Kirkpatrick
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We present a model and methodology for learning paraphrastic sentence embeddings directly from bitext, removing the time-consuming intermediate step of creating para-phrase corpora. Further, we show that the resulting model can be applied to cross lingual tasks where it both outperforms and is orders of magnitude faster than more complex state-of-the-art baselines.

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Improving Open Information Extraction via Iterative Rank-Aware Learning
Zhengbao Jiang | Pengcheng Yin | Graham Neubig
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Open information extraction (IE) is the task of extracting open-domain assertions from natural language sentences. A key step in open IE is confidence modeling, ranking the extractions based on their estimated quality to adjust precision and recall of extracted assertions. We found that the extraction likelihood, a confidence measure used by current supervised open IE systems, is not well calibrated when comparing the quality of assertions extracted from different sentences. We propose an additional binary classification loss to calibrate the likelihood to make it more globally comparable, and an iterative learning process, where extractions generated by the open IE model are incrementally included as training samples to help the model learn from trial and error. Experiments on OIE2016 demonstrate the effectiveness of our method. Code and data are available at https://github.com/jzbjyb/oie_rank.

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Generalized Data Augmentation for Low-Resource Translation
Mengzhou Xia | Xiang Kong | Antonios Anastasopoulos | Graham Neubig
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Low-resource language pairs with a paucity of parallel data pose challenges for machine translation in terms of both adequacy and fluency. Data augmentation utilizing a large amount of monolingual data is regarded as an effective way to alleviate the problem. In this paper, we propose a general framework of data augmentation for low-resource machine translation not only using target-side monolingual data, but also by pivoting through a related high-resource language. Specifically, we experiment with a two-step pivoting method to convert high-resource data to the low-resource language, making best use of available resources to better approximate the true distribution of the low-resource language. First, we inject low-resource words into high-resource sentences through an induced bilingual dictionary. Second, we further edit the high-resource data injected with low-resource words using a modified unsupervised machine translation framework. Extensive experiments on four low-resource datasets show that under extreme low-resource settings, our data augmentation techniques improve translation quality by up to 1.5 to 8 BLEU points compared to supervised back-translation baselines.

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Target Conditioned Sampling: Optimizing Data Selection for Multilingual Neural Machine Translation
Xinyi Wang | Graham Neubig
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

To improve low-resource Neural Machine Translation (NMT) with multilingual corpus, training on the most related high-resource language only is generally more effective than us- ing all data available (Neubig and Hu, 2018). However, it remains a question whether a smart data selection strategy can further improve low-resource NMT with data from other auxiliary languages. In this paper, we seek to construct a sampling distribution over all multilingual data, so that it minimizes the training loss of the low-resource language. Based on this formulation, we propose and efficient algorithm, (TCS), which first samples a target sentence, and then conditionally samples its source sentence. Experiments show TCS brings significant gains of up to 2 BLEU improvements on three of four languages we test, with minimal training overhead.

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Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation
Matthias Sperber | Graham Neubig | Jan Niehues | Alex Waibel
Transactions of the Association for Computational Linguistics, Volume 7

Speech translation has traditionally been approached through cascaded models consisting of a speech recognizer trained on a corpus of transcribed speech, and a machine translation system trained on parallel texts. Several recent works have shown the feasibility of collapsing the cascade into a single, direct model that can be trained in an end-to-end fashion on a corpus of translated speech. However, experiments are inconclusive on whether the cascade or the direct model is stronger, and have only been conducted under the unrealistic assumption that both are trained on equal amounts of data, ignoring other available speech recognition and machine translation corpora. In this paper, we demonstrate that direct speech translation models require more data to perform well than cascaded models, and although they allow including auxiliary data through multi-task training, they are poor at exploiting such data, putting them at a severe disadvantage. As a remedy, we propose the use of end- to-end trainable models with two attention mechanisms, the first establishing source speech to source text alignments, the second modeling source to target text alignment. We show that such models naturally decompose into multi-task–trainable recognition and translation tasks and propose an attention-passing technique that alleviates error propagation issues in a previous formulation of a model with two attention stages. Our proposed model outperforms all examined baselines and is able to exploit auxiliary training data much more effectively than direct attentional models.

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Comparing Top-Down and Bottom-Up Neural Generative Dependency Models
Austin Matthews | Graham Neubig | Chris Dyer
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Recurrent neural network grammars generate sentences using phrase-structure syntax and perform very well on both parsing and language modeling. To explore whether generative dependency models are similarly effective, we propose two new generative models of dependency syntax. Both models use recurrent neural nets to avoid making explicit independence assumptions, but they differ in the order used to construct the trees: one builds the tree bottom-up and the other top-down, which profoundly changes the estimation problem faced by the learner. We evaluate the two models on three typologically different languages: English, Arabic, and Japanese. While both generative models improve parsing performance over a discriminative baseline, they are significantly less effective than non-syntactic LSTM language models. Surprisingly, little difference between the construction orders is observed for either parsing or language modeling.

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Lost in Interpretation: Predicting Untranslated Terminology in Simultaneous Interpretation
Nikolai Vogler | Craig Stewart | Graham Neubig
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Simultaneous interpretation, the translation of speech from one language to another in real-time, is an inherently difficult and strenuous task. One of the greatest challenges faced by interpreters is the accurate translation of difficult terminology like proper names, numbers, or other entities. Intelligent computer-assisted interpreting (CAI) tools that could analyze the spoken word and detect terms likely to be untranslated by an interpreter could reduce translation error and improve interpreter performance. In this paper, we propose a task of predicting which terminology simultaneous interpreters will leave untranslated, and examine methods that perform this task using supervised sequence taggers. We describe a number of task-specific features explicitly designed to indicate when an interpreter may struggle with translating a word. Experimental results on a newly-annotated version of the NAIST Simultaneous Translation Corpus (Shimizu et al., 2014) indicate the promise of our proposed method.

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Competence-based Curriculum Learning for Neural Machine Translation
Emmanouil Antonios Platanios | Otilia Stretcu | Graham Neubig | Barnabas Poczos | Tom Mitchell
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Current state-of-the-art NMT systems use large neural networks that are not only slow to train, but also often require many heuristics and optimization tricks, such as specialized learning rate schedules and large batch sizes. This is undesirable as it requires extensive hyperparameter tuning. In this paper, we propose a curriculum learning framework for NMT that reduces training time, reduces the need for specialized heuristics or large batch sizes, and results in overall better performance. Our framework consists of a principled way of deciding which training samples are shown to the model at different times during training, based on the estimated difficulty of a sample and the current competence of the model. Filtering training samples in this manner prevents the model from getting stuck in bad local optima, making it converge faster and reach a better solution than the common approach of uniformly sampling training examples. Furthermore, the proposed method can be easily applied to existing NMT models by simply modifying their input data pipelines. We show that our framework can help improve the training time and the performance of both recurrent neural network models and Transformers, achieving up to a 70% decrease in training time, while at the same time obtaining accuracy improvements of up to 2.2 BLEU.

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Density Matching for Bilingual Word Embedding
Chunting Zhou | Xuezhe Ma | Di Wang | Graham Neubig
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Recent approaches to cross-lingual word embedding have generally been based on linear transformations between the sets of embedding vectors in the two languages. In this paper, we propose an approach that instead expresses the two monolingual embedding spaces as probability densities defined by a Gaussian mixture model, and matches the two densities using a method called normalizing flow. The method requires no explicit supervision, and can be learned with only a seed dictionary of words that have identical strings. We argue that this formulation has several intuitively attractive properties, particularly with the respect to improving robustness and generalization to mappings between difficult language pairs or word pairs. On a benchmark data set of bilingual lexicon induction and cross-lingual word similarity, our approach can achieve competitive or superior performance compared to state-of-the-art published results, with particularly strong results being found on etymologically distant and/or morphologically rich languages.

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Improving Robustness of Machine Translation with Synthetic Noise
Vaibhav Vaibhav | Sumeet Singh | Craig Stewart | Graham Neubig
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Modern Machine Translation (MT) systems perform remarkably well on clean, in-domain text. However most of the human generated text, particularly in the realm of social media, is full of typos, slang, dialect, idiolect and other noise which can have a disastrous impact on the accuracy of MT. In this paper we propose methods to enhance the robustness of MT systems by emulating naturally occurring noise in otherwise clean data. Synthesizing noise in this manner we are ultimately able to make a vanilla MT system more resilient to naturally occurring noise, partially mitigating loss in accuracy resulting therefrom.

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On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models
Paul Michel | Xian Li | Graham Neubig | Juan Pino
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Adversarial examples — perturbations to the input of a model that elicit large changes in the output — have been shown to be an effective way of assessing the robustness of sequence-to-sequence (seq2seq) models. However, these perturbations only indicate weaknesses in the model if they do not change the input so significantly that it legitimately results in changes in the expected output. This fact has largely been ignored in the evaluations of the growing body of related literature. Using the example of untargeted attacks on machine translation (MT), we propose a new evaluation framework for adversarial attacks on seq2seq models that takes the semantic equivalence of the pre- and post-perturbation input into account. Using this framework, we demonstrate that existing methods may not preserve meaning in general, breaking the aforementioned assumption that source side perturbations should not result in changes in the expected output. We further use this framework to demonstrate that adding additional constraints on attacks allows for adversarial perturbations that are more meaning-preserving, but nonetheless largely change the output sequence. Finally, we show that performing untargeted adversarial training with meaning-preserving attacks is beneficial to the model in terms of adversarial robustness, without hurting test performance. A toolkit implementing our evaluation framework is released at https://github.com/pmichel31415/teapot-nlp.

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Learning to Describe Unknown Phrases with Local and Global Contexts
Shonosuke Ishiwatari | Hiroaki Hayashi | Naoki Yoshinaga | Graham Neubig | Shoetsu Sato | Masashi Toyoda | Masaru Kitsuregawa
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

When reading a text, it is common to become stuck on unfamiliar words and phrases, such as polysemous words with novel senses, rarely used idioms, internet slang, or emerging entities. If we humans cannot figure out the meaning of those expressions from the immediate local context, we consult dictionaries for definitions or search documents or the web to find other global context to help in interpretation. Can machines help us do this work? Which type of context is more important for machines to solve the problem? To answer these questions, we undertake a task of describing a given phrase in natural language based on its local and global contexts. To solve this task, we propose a neural description model that consists of two context encoders and a description decoder. In contrast to the existing methods for non-standard English explanation [Ni+ 2017] and definition generation [Noraset+ 2017; Gadetsky+ 2018], our model appropriately takes important clues from both local and global contexts. Experimental results on three existing datasets (including WordNet, Oxford and Urban Dictionaries) and a dataset newly created from Wikipedia demonstrate the effectiveness of our method over previous work.

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compare-mt: A Tool for Holistic Comparison of Language Generation Systems
Graham Neubig | Zi-Yi Dou | Junjie Hu | Paul Michel | Danish Pruthi | Xinyi Wang
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)

In this paper, we describe compare-mt, a tool for holistic analysis and comparison of the results of systems for language generation tasks such as machine translation. The main goal of the tool is to give the user a high-level and coherent view of the salient differences between systems that can then be used to guide further analysis or system improvement. It implements a number of tools to do so, such as analysis of accuracy of generation of particular types of words, bucketed histograms of sentence accuracies or counts based on salient characteristics, and extraction of characteristic n-grams for each system. It also has a number of advanced features such as use of linguistic labels, source side data, or comparison of log likelihoods for probabilistic models, and also aims to be easily extensible by users to new types of analysis. compare-mt is a pure-Python open source package, that has already proven useful to generate analyses that have been used in our published papers. Demo Video: https://youtu.be/NyJEQT7t2CA

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Pushing the Limits of Low-Resource Morphological Inflection
Antonios Anastasopoulos | Graham Neubig
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Recent years have seen exceptional strides in the task of automatic morphological inflection generation. However, for a long tail of languages the necessary resources are hard to come by, and state-of-the-art neural methods that work well under higher resource settings perform poorly in the face of a paucity of data. In response, we propose a battery of improvements that greatly improve performance under such low-resource conditions. First, we present a novel two-step attention architecture for the inflection decoder. In addition, we investigate the effects of cross-lingual transfer from single and multiple languages, as well as monolingual data hallucination. The macro-averaged accuracy of our models outperforms the state-of-the-art by 15 percentage points. Also, we identify the crucial factors for success with cross-lingual transfer for morphological inflection: typological similarity and a common representation across languages.

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Handling Syntactic Divergence in Low-resource Machine Translation
Chunting Zhou | Xuezhe Ma | Junjie Hu | Graham Neubig
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Despite impressive empirical successes of neural machine translation (NMT) on standard benchmarks, limited parallel data impedes the application of NMT models to many language pairs. Data augmentation methods such as back-translation make it possible to use monolingual data to help alleviate these issues, but back-translation itself fails in extreme low-resource scenarios, especially for syntactically divergent languages. In this paper, we propose a simple yet effective solution, whereby target-language sentences are re-ordered to match the order of the source and used as an additional source of training-time supervision. Experiments with simulated low-resource Japanese-to-English, and real low-resource Uyghur-to-English scenarios find significant improvements over other semi-supervised alternatives.

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Unsupervised Domain Adaptation for Neural Machine Translation with Domain-Aware Feature Embeddings
Zi-Yi Dou | Junjie Hu | Antonios Anastasopoulos | Graham Neubig
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

The recent success of neural machine translation models relies on the availability of high quality, in-domain data. Domain adaptation is required when domain-specific data is scarce or nonexistent. Previous unsupervised domain adaptation strategies include training the model with in-domain copied monolingual or back-translated data. However, these methods use generic representations for text regardless of domain shift, which makes it infeasible for translation models to control outputs conditional on a specific domain. In this work, we propose an approach that adapts models with domain-aware feature embeddings, which are learned via an auxiliary language modeling task. Our approach allows the model to assign domain-specific representations to words and output sentences in the desired domain. Our empirical results demonstrate the effectiveness of the proposed strategy, achieving consistent improvements in multiple experimental settings. In addition, we show that combining our method with back translation can further improve the performance of the model.

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A Surprisingly Effective Fix for Deep Latent Variable Modeling of Text
Bohan Li | Junxian He | Graham Neubig | Taylor Berg-Kirkpatrick | Yiming Yang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

When trained effectively, the Variational Autoencoder (VAE) is both a powerful language model and an effective representation learning framework. In practice, however, VAEs are trained with the evidence lower bound (ELBO) as a surrogate objective to the intractable marginal data likelihood. This approach to training yields unstable results, frequently leading to a disastrous local optimum known as posterior collapse. In this paper, we investigate a simple fix for posterior collapse which yields surprisingly effective results. The combination of two known heuristics, previously considered only in isolation, substantially improves held-out likelihood, reconstruction, and latent representation learning when compared with previous state-of-the-art methods. More interestingly, while our experiments demonstrate superiority on these principle evaluations, our method obtains a worse ELBO. We use these results to argue that the typical surrogate objective for VAEs may not be sufficient or necessarily appropriate for balancing the goals of representation learning and data distribution modeling.

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FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow
Xuezhe Ma | Chunting Zhou | Xian Li | Graham Neubig | Eduard Hovy
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Most sequence-to-sequence (seq2seq) models are autoregressive; they generate each token by conditioning on previously generated tokens. In contrast, non-autoregressive seq2seq models generate all tokens in one pass, which leads to increased efficiency through parallel processing on hardware such as GPUs. However, directly modeling the joint distribution of all tokens simultaneously is challenging, and even with increasingly complex model structures accuracy lags significantly behind autoregressive models. In this paper, we propose a simple, efficient, and effective model for non-autoregressive sequence generation using latent variable models. Specifically, we turn to generative flow, an elegant technique to model complex distributions using neural networks, and design several layers of flow tailored for modeling the conditional density of sequential latent variables. We evaluate this model on three neural machine translation (NMT) benchmark datasets, achieving comparable performance with state-of-the-art non-autoregressive NMT models and almost constant decoding time w.r.t the sequence length.

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A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers
Aditi Chaudhary | Jiateng Xie | Zaid Sheikh | Graham Neubig | Jaime Carbonell
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Most state-of-the-art models for named entity recognition (NER) rely on the availability of large amounts of labeled data, making them challenging to extend to new, lower-resourced languages. However, there are now many proposed solutions to this problem involving either cross-lingual transfer learning, which learns from other highly resourced languages, or active learning, which efficiently selects effective training data based on model predictions. In this paper, we ask the question: given this recent progress, and some amount of human annotation, what is the most effective method for efficiently creating high-quality entity recognizers in under-resourced languages? Based on extensive experimentation using both simulated and real human annotation, we settle on a recipe of starting with a cross-lingual transferred model, then performing targeted annotation of only uncertain entity spans in the target language, minimizing annotator effort. Results demonstrate that cross-lingual transfer is a powerful tool when very little data can be annotated, but an entity-targeted annotation strategy can achieve competitive accuracy quickly, with just one-tenth of training data.

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Proceedings of the 3rd Workshop on Neural Generation and Translation
Alexandra Birch | Andrew Finch | Hiroaki Hayashi | Ioannis Konstas | Thang Luong | Graham Neubig | Yusuke Oda | Katsuhito Sudoh
Proceedings of the 3rd Workshop on Neural Generation and Translation

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Findings of the Third Workshop on Neural Generation and Translation
Hiroaki Hayashi | Yusuke Oda | Alexandra Birch | Ioannis Konstas | Andrew Finch | Minh-Thang Luong | Graham Neubig | Katsuhito Sudoh
Proceedings of the 3rd Workshop on Neural Generation and Translation

This document describes the findings of the Third Workshop on Neural Generation and Translation, held in concert with the annual conference of the Empirical Methods in Natural Language Processing (EMNLP 2019). First, we summarize the research trends of papers presented in the proceedings. Second, we describe the results of the two shared tasks 1) efficient neural machine translation (NMT) where participants were tasked with creating NMT systems that are both accurate and efficient, and 2) document generation and translation (DGT) where participants were tasked with developing systems that generate summaries from structured data, potentially with assistance from text in another language.

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Domain Differential Adaptation for Neural Machine Translation
Zi-Yi Dou | Xinyi Wang | Junjie Hu | Graham Neubig
Proceedings of the 3rd Workshop on Neural Generation and Translation

Neural networks are known to be data hungry and domain sensitive, but it is nearly impossible to obtain large quantities of labeled data for every domain we are interested in. This necessitates the use of domain adaptation strategies. One common strategy encourages generalization by aligning the global distribution statistics between source and target domains, but one drawback is that the statistics of different domains or tasks are inherently divergent, and smoothing over these differences can lead to sub-optimal performance. In this paper, we propose the framework of Domain Differential Adaptation (DDA), where instead of smoothing over these differences we embrace them, directly modeling the difference between domains using models in a related task. We then use these learned domain differentials to adapt models for the target task accordingly. Experimental results on domain adaptation for neural machine translation demonstrate the effectiveness of this strategy, achieving consistent improvements over other alternative adaptation strategies in multiple experimental settings.

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Towards Zero-resource Cross-lingual Entity Linking
Shuyan Zhou | Shruti Rijhwani | Graham Neubig
Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)

Cross-lingual entity linking (XEL) grounds named entities in a source language to an English Knowledge Base (KB), such as Wikipedia. XEL is challenging for most languages because of limited availability of requisite resources. However, many works on XEL have been on simulated settings that actually use significant resources (e.g. source language Wikipedia, bilingual entity maps, multilingual embeddings) that are not available in truly low-resource languages. In this work, we first examine the effect of these resource assumptions and quantify how much the availability of these resource affects overall quality of existing XEL systems. We next propose three improvements to both entity candidate generation and disambiguation that make better use of the limited resources we do have in resource-scarce scenarios. With experiments on four extremely low-resource languages, we show that our model results in gains of 6-20% end-to-end linking accuracy.

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Findings of the First Shared Task on Machine Translation Robustness
Xian Li | Paul Michel | Antonios Anastasopoulos | Yonatan Belinkov | Nadir Durrani | Orhan Firat | Philipp Koehn | Graham Neubig | Juan Pino | Hassan Sajjad
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

We share the findings of the first shared task on improving robustness of Machine Translation (MT). The task provides a testbed representing challenges facing MT models deployed in the real world, and facilitates new approaches to improve models’ robustness to noisy input and domain mismatch. We focus on two language pairs (English-French and English-Japanese), and the submitted systems are evaluated on a blind test set consisting of noisy comments on Reddit and professionally sourced translations. As a new task, we received 23 submissions by 11 participating teams from universities, companies, national labs, etc. All submitted systems achieved large improvements over baselines, with the best improvement having +22.33 BLEU. We evaluated submissions by both human judgment and automatic evaluation (BLEU), which shows high correlations (Pearson’s r = 0.94 and 0.95). Furthermore, we conducted a qualitative analysis of the submitted systems using compare-mt, which revealed their salient differences in handling challenges in this task. Such analysis provides additional insights when there is occasional disagreement between human judgment and BLEU, e.g. systems better at producing colloquial expressions received higher score from human judgment.

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Improving Robustness of Neural Machine Translation with Multi-task Learning
Shuyan Zhou | Xiangkai Zeng | Yingqi Zhou | Antonios Anastasopoulos | Graham Neubig
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

While neural machine translation (NMT) achieves remarkable performance on clean, in-domain text, performance is known to degrade drastically when facing text which is full of typos, grammatical errors and other varieties of noise. In this work, we propose a multi-task learning algorithm for transformer-based MT systems that is more resilient to this noise. We describe our submission to the WMT 2019 Robustness shared task based on this method. Our model achieves a BLEU score of 32.8 on the shared task French to English dataset, which is 7.1 BLEU points higher than the baseline vanilla transformer trained with clean text.

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Contextualized Representations for Low-resource Utterance Tagging
Bhargavi Paranjape | Graham Neubig
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue

Utterance-level analysis of the speaker’s intentions and emotions is a core task in conversational understanding. Depending on the end objective of the conversational understanding task, different categorical dialog-act or affect labels are expertly designed to cover specific aspects of the speakers’ intentions or emotions respectively. Accurately annotating with these labels requires a high level of human expertise, and thus applying this process to a large conversation corpus or new domains is prohibitively expensive. The resulting paucity of data limits the use of sophisticated neural models. In this paper, we tackle these limitations by performing unsupervised training of utterance representations from a large corpus of spontaneous dialogue data. Models initialized with these representations achieve competitive performance on utterance-level dialogue-act recognition and emotion classification, especially in low-resource settings encountered when analyzing conversations in new domains.

2018

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Attentive Interaction Model: Modeling Changes in View in Argumentation
Yohan Jo | Shivani Poddar | Byungsoo Jeon | Qinlan Shen | Carolyn Rosé | Graham Neubig
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

We present a neural architecture for modeling argumentative dialogue that explicitly models the interplay between an Opinion Holder’s (OH’s) reasoning and a challenger’s argument, with the goal of predicting if the argument successfully changes the OH’s view. The model has two components: (1) vulnerable region detection, an attention model that identifies parts of the OH’s reasoning that are amenable to change, and (2) interaction encoding, which identifies the relationship between the content of the OH’s reasoning and that of the challenger’s argument. Based on evaluation on discussions from the Change My View forum on Reddit, the two components work together to predict an OH’s change in view, outperforming several baselines. A posthoc analysis suggests that sentences picked out by the attention model are addressed more frequently by successful arguments than by unsuccessful ones.

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Guiding Neural Machine Translation with Retrieved Translation Pieces
Jingyi Zhang | Masao Utiyama | Eiichro Sumita | Graham Neubig | Satoshi Nakamura
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

One of the difficulties of neural machine translation (NMT) is the recall and appropriate translation of low-frequency words or phrases. In this paper, we propose a simple, fast, and effective method for recalling previously seen translation examples and incorporating them into the NMT decoding process. Specifically, for an input sentence, we use a search engine to retrieve sentence pairs whose source sides are similar with the input sentence, and then collect n-grams that are both in the retrieved target sentences and aligned with words that match in the source sentences, which we call “translation pieces”. We compute pseudo-probabilities for each retrieved sentence based on similarities between the input sentence and the retrieved source sentences, and use these to weight the retrieved translation pieces. Finally, an existing NMT model is used to translate the input sentence, with an additional bonus given to outputs that contain the collected translation pieces. We show our method improves NMT translation results up to 6 BLEU points on three narrow domain translation tasks where repetitiveness of the target sentences is particularly salient. It also causes little increase in the translation time, and compares favorably to another alternative retrieval-based method with respect to accuracy, speed, and simplicity of implementation.

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Handling Homographs in Neural Machine Translation
Frederick Liu | Han Lu | Graham Neubig
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Homographs, words with different meanings but the same surface form, have long caused difficulty for machine translation systems, as it is difficult to select the correct translation based on the context. However, with the advent of neural machine translation (NMT) systems, which can theoretically take into account global sentential context, one may hypothesize that this problem has been alleviated. In this paper, we first provide empirical evidence that existing NMT systems in fact still have significant problems in properly translating ambiguous words. We then proceed to describe methods, inspired by the word sense disambiguation literature, that model the context of the input word with context-aware word embeddings that help to differentiate the word sense before feeding it into the encoder. Experiments on three language pairs demonstrate that such models improve the performance of NMT systems both in terms of BLEU score and in the accuracy of translating homographs.

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Using Morphological Knowledge in Open-Vocabulary Neural Language Models
Austin Matthews | Graham Neubig | Chris Dyer
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Languages with productive morphology pose problems for language models that generate words from a fixed vocabulary. Although character-based models allow any possible word type to be generated, they are linguistically naïve: they must discover that words exist and are delimited by spaces—basic linguistic facts that are built in to the structure of word-based models. We introduce an open-vocabulary language model that incorporates more sophisticated linguistic knowledge by predicting words using a mixture of three generative processes: (1) by generating words as a sequence of characters, (2) by directly generating full word forms, and (3) by generating words as a sequence of morphemes that are combined using a hand-written morphological analyzer. Experiments on Finnish, Turkish, and Russian show that our model outperforms character sequence models and other strong baselines on intrinsic and extrinsic measures. Furthermore, we show that our model learns to exploit morphological knowledge encoded in the analyzer, and, as a byproduct, it can perform effective unsupervised morphological disambiguation.

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When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?
Ye Qi | Devendra Sachan | Matthieu Felix | Sarguna Padmanabhan | Graham Neubig
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

The performance of Neural Machine Translation (NMT) systems often suffers in low-resource scenarios where sufficiently large-scale parallel corpora cannot be obtained. Pre-trained word embeddings have proven to be invaluable for improving performance in natural language analysis tasks, which often suffer from paucity of data. However, their utility for NMT has not been extensively explored. In this work, we perform five sets of experiments that analyze when we can expect pre-trained word embeddings to help in NMT tasks. We show that such embeddings can be surprisingly effective in some cases – providing gains of up to 20 BLEU points in the most favorable setting.

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Modelling Natural Language, Programs, and their Intersection
Graham Neubig | Miltiadis Allamanis
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorial Abstracts

As computers and information grow a more integral part of our world, it is becoming more and more important for humans to be able to interact with their computers in complex ways. One way to do so is by programming, but the ability to understand and generate programming languages is a highly specialized skill. As a result, in the past several years there has been an increasing research interest in methods that focus on the intersection of programming and natural language, allowing users to use natural language to interact with computers in the complex ways that programs allow us to do. In this tutorial, we will focus on machine learning models of programs and natural language focused on making this goal a reality. First, we will discuss the similarities and differences between programming and natural language. Then we will discuss methods that have been designed to cover a variety of tasks in this field, including automatic explanation of programs in natural language (code-to-language), automatic generation of programs from natural language specifications (language-to-code), modeling the natural language elements of source code, and analysis of communication in collaborative programming communities. The tutorial will be aimed at NLP researchers and practitioners, aiming to describe the interesting opportunities that models at the intersection of natural and programming languages provide, and also how their techniques could provide benefit to the practice of software engineering as a whole.

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Multi-Source Neural Machine Translation with Data Augmentation
Yuta Nishimura | Katsuhito Sudoh | Graham Neubig | Satoshi Nakamura
Proceedings of the 15th International Conference on Spoken Language Translation

Multi-source translation systems translate from multiple languages to a single target language. By using information from these multiple sources, these systems achieve large gains in accuracy. To train these systems, it is necessary to have corpora with parallel text in multiple sources and the target language. However, these corpora are rarely complete in practice due to the difficulty of providing human translations in all of the relevant languages. In this paper, we propose a data augmentation approach to fill such incomplete parts using multi-source neural machine translation (NMT). In our experiments, results varied over different language combinations but significant gains were observed when using a source language similar to the target language.

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StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing
Pengcheng Yin | Chunting Zhou | Junxian He | Graham Neubig
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures. Annotating NL utterances with their corresponding MRs is expensive and time-consuming, and thus the limited availability of labeled data often becomes the bottleneck of data-driven, supervised models. We introduce StructVAE, a variational auto-encoding model for semi-supervised semantic parsing, which learns both from limited amounts of parallel data, and readily-available unlabeled NL utterances. StructVAE models latent MRs not observed in the unlabeled data as tree-structured latent variables. Experiments on semantic parsing on the ATIS domain and Python code generation show that with extra unlabeled data, StructVAE outperforms strong supervised models.

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Stack-Pointer Networks for Dependency Parsing
Xuezhe Ma | Zecong Hu | Jingzhou Liu | Nanyun Peng | Graham Neubig | Eduard Hovy
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We introduce a novel architecture for dependency parsing: stack-pointer networks (StackPtr). Combining pointer networks (Vinyals et al., 2015) with an internal stack, the proposed model first reads and encodes the whole sentence, then builds the dependency tree top-down (from root-to-leaf) in a depth-first fashion. The stack tracks the status of the depth-first search and the pointer networks select one child for the word at the top of the stack at each step. The StackPtr parser benefits from the information of whole sentence and all previously derived subtree structures, and removes the left-to-right restriction in classical transition-based parsers. Yet the number of steps for building any (non-projective) parse tree is linear in the length of the sentence just as other transition-based parsers, yielding an efficient decoding algorithm with O(n2) time complexity. We evaluate our model on 29 treebanks spanning 20 languages and different dependency annotation schemas, and achieve state-of-the-art performances on 21 of them

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Learning to Generate Move-by-Move Commentary for Chess Games from Large-Scale Social Forum Data
Harsh Jhamtani | Varun Gangal | Eduard Hovy | Graham Neubig | Taylor Berg-Kirkpatrick
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

This paper examines the problem of generating natural language descriptions of chess games. We introduce a new large-scale chess commentary dataset and propose methods to generate commentary for individual moves in a chess game. The introduced dataset consists of more than 298K chess move-commentary pairs across 11K chess games. We highlight how this task poses unique research challenges in natural language generation: the data contain a large variety of styles of commentary and frequently depend on pragmatic context. We benchmark various baselines and propose an end-to-end trainable neural model which takes into account multiple pragmatic aspects of the game state that may be commented upon to describe a given chess move. Through a human study on predictions for a subset of the data which deals with direct move descriptions, we observe that outputs from our models are rated similar to ground truth commentary texts in terms of correctness and fluency.

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Neural Factor Graph Models for Cross-lingual Morphological Tagging
Chaitanya Malaviya | Matthew R. Gormley | Graham Neubig
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Morphological analysis involves predicting the syntactic traits of a word (e.g. POS: Noun, Case: Acc, Gender: Fem). Previous work in morphological tagging improves performance for low-resource languages (LRLs) through cross-lingual training with a high-resource language (HRL) from the same family, but is limited by the strict, often false, assumption that tag sets exactly overlap between the HRL and LRL. In this paper we propose a method for cross-lingual morphological tagging that aims to improve information sharing between languages by relaxing this assumption. The proposed model uses factorial conditional random fields with neural network potentials, making it possible to (1) utilize the expressive power of neural network representations to smooth over superficial differences in the surface forms, (2) model pairwise and transitive relationships between tags, and (3) accurately generate tag sets that are unseen or rare in the training data. Experiments on four languages from the Universal Dependencies Treebank demonstrate superior tagging accuracies over existing cross-lingual approaches.

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Extreme Adaptation for Personalized Neural Machine Translation
Paul Michel | Graham Neubig
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Every person speaks or writes their own flavor of their native language, influenced by a number of factors: the content they tend to talk about, their gender, their social status, or their geographical origin. When attempting to perform Machine Translation (MT), these variations have a significant effect on how the system should perform translation, but this is not captured well by standard one-size-fits-all models. In this paper, we propose a simple and parameter-efficient adaptation technique that only requires adapting the bias of the output softmax to each particular user of the MT system, either directly or through a factored approximation. Experiments on TED talks in three languages demonstrate improvements in translation accuracy, and better reflection of speaker traits in the target text.

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Automatic Estimation of Simultaneous Interpreter Performance
Craig Stewart | Nikolai Vogler | Junjie Hu | Jordan Boyd-Graber | Graham Neubig
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Simultaneous interpretation, translation of the spoken word in real-time, is both highly challenging and physically demanding. Methods to predict interpreter confidence and the adequacy of the interpreted message have a number of potential applications, such as in computer-assisted interpretation interfaces or pedagogical tools. We propose the task of predicting simultaneous interpreter performance by building on existing methodology for quality estimation (QE) of machine translation output. In experiments over five settings in three language pairs, we extend a QE pipeline to estimate interpreter performance (as approximated by the METEOR evaluation metric) and propose novel features reflecting interpretation strategy and evaluation measures that further improve prediction accuracy.

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Stress Test Evaluation for Natural Language Inference
Aakanksha Naik | Abhilasha Ravichander | Norman Sadeh | Carolyn Rose | Graham Neubig
Proceedings of the 27th International Conference on Computational Linguistics

Natural language inference (NLI) is the task of determining if a natural language hypothesis can be inferred from a given premise in a justifiable manner. NLI was proposed as a benchmark task for natural language understanding. Existing models perform well at standard datasets for NLI, achieving impressive results across different genres of text. However, the extent to which these models understand the semantic content of sentences is unclear. In this work, we propose an evaluation methodology consisting of automatically constructed “stress tests” that allow us to examine whether systems have the ability to make real inferential decisions. Our evaluation of six sentence-encoder models on these stress tests reveals strengths and weaknesses of these models with respect to challenging linguistic phenomena, and suggests important directions for future work in this area.

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Evaluation Phonemic Transcription of Low-Resource Tonal Languages for Language Documentation
Oliver Adams | Trevor Cohn | Graham Neubig | Hilaria Cruz | Steven Bird | Alexis Michaud
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Neural Lattice Language Models
Jacob Buckman | Graham Neubig
Transactions of the Association for Computational Linguistics, Volume 6

In this work, we propose a new language modeling paradigm that has the ability to perform both prediction and moderation of information flow at multiple granularities: neural lattice language models. These models construct a lattice of possible paths through a sentence and marginalize across this lattice to calculate sequence probabilities or optimize parameters. This approach allows us to seamlessly incorporate linguistic intuitions — including polysemy and the existence of multiword lexical items — into our language model. Experiments on multiple language modeling tasks show that English neural lattice language models that utilize polysemous embeddings are able to improve perplexity by 9.95% relative to a word-level baseline, and that a Chinese model that handles multi-character tokens is able to improve perplexity by 20.94% relative to a character-level baseline.

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Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)
Colin Cherry | Graham Neubig
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

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XNMT: The eXtensible Neural Machine Translation Toolkit
Graham Neubig | Matthias Sperber | Xinyi Wang | Matthieu Felix | Austin Matthews | Sarguna Padmanabhan | Ye Qi | Devendra Sachan | Philip Arthur | Pierre Godard | John Hewitt | Rachid Riad | Liming Wang
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

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Proceedings of the 2nd Workshop on Neural Machine Translation and Generation
Alexandra Birch | Andrew Finch | Thang Luong | Graham Neubig | Yusuke Oda
Proceedings of the 2nd Workshop on Neural Machine Translation and Generation

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Findings of the Second Workshop on Neural Machine Translation and Generation
Alexandra Birch | Andrew Finch | Minh-Thang Luong | Graham Neubig | Yusuke Oda
Proceedings of the 2nd Workshop on Neural Machine Translation and Generation

This document describes the findings of the Second Workshop on Neural Machine Translation and Generation, held in concert with the annual conference of the Association for Computational Linguistics (ACL 2018). First, we summarize the research trends of papers presented in the proceedings, and note that there is particular interest in linguistic structure, domain adaptation, data augmentation, handling inadequate resources, and analysis of models. Second, we describe the results of the workshop’s shared task on efficient neural machine translation, where participants were tasked with creating MT systems that are both accurate and efficient.

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Multi-Source Neural Machine Translation with Missing Data
Yuta Nishimura | Katsuhito Sudoh | Graham Neubig | Satoshi Nakamura
Proceedings of the 2nd Workshop on Neural Machine Translation and Generation

Multi-source translation is an approach to exploit multiple inputs (e.g. in two different languages) to increase translation accuracy. In this paper, we examine approaches for multi-source neural machine translation (NMT) using an incomplete multilingual corpus in which some translations are missing. In practice, many multilingual corpora are not complete due to the difficulty to provide translations in all of the relevant languages (for example, in TED talks, most English talks only have subtitles for a small portion of the languages that TED supports). Existing studies on multi-source translation did not explicitly handle such situations. This study focuses on the use of incomplete multilingual corpora in multi-encoder NMT and mixture of NMT experts and examines a very simple implementation where missing source translations are replaced by a special symbol <NULL>. These methods allow us to use incomplete corpora both at training time and test time. In experiments with real incomplete multilingual corpora of TED Talks, the multi-source NMT with the <NULL> tokens achieved higher translation accuracies measured by BLEU than those by any one-to-one NMT systems.

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Parameter Sharing Methods for Multilingual Self-Attentional Translation Models
Devendra Sachan | Graham Neubig
Proceedings of the Third Conference on Machine Translation: Research Papers

In multilingual neural machine translation, it has been shown that sharing a single translation model between multiple languages can achieve competitive performance, sometimes even leading to performance gains over bilingually trained models. However, these improvements are not uniform; often multilingual parameter sharing results in a decrease in accuracy due to translation models not being able to accommodate different languages in their limited parameter space. In this work, we examine parameter sharing techniques that strike a happy medium between full sharing and individual training, specifically focusing on the self-attentional Transformer model. We find that the full parameter sharing approach leads to increases in BLEU scores mainly when the target languages are from a similar language family. However, even in the case where target languages are from different families where full parameter sharing leads to a noticeable drop in BLEU scores, our proposed methods for partial sharing of parameters can lead to substantial improvements in translation accuracy.

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Contextual Encoding for Translation Quality Estimation
Junjie Hu | Wei-Cheng Chang | Yuexin Wu | Graham Neubig
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

The task of word-level quality estimation (QE) consists of taking a source sentence and machine-generated translation, and predicting which words in the output are correct and which are wrong. In this paper, propose a method to effectively encode the local and global contextual information for each target word using a three-part neural network approach. The first part uses an embedding layer to represent words and their part-of-speech tags in both languages. The second part leverages a one-dimensional convolution layer to integrate local context information for each target word. The third part applies a stack of feed-forward and recurrent neural networks to further encode the global context in the sentence before making the predictions. This model was submitted as the CMU entry to the WMT2018 shared task on QE, and achieves strong results, ranking first in three of the six tracks.

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Neural Cross-Lingual Named Entity Recognition with Minimal Resources
Jiateng Xie | Zhilin Yang | Graham Neubig | Noah A. Smith | Jaime Carbonell
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

For languages with no annotated resources, unsupervised transfer of natural language processing models such as named-entity recognition (NER) from resource-rich languages would be an appealing capability. However, differences in words and word order across languages make it a challenging problem. To improve mapping of lexical items across languages, we propose a method that finds translations based on bilingual word embeddings. To improve robustness to word order differences, we propose to use self-attention, which allows for a degree of flexibility with respect to word order. We demonstrate that these methods achieve state-of-the-art or competitive NER performance on commonly tested languages under a cross-lingual setting, with much lower resource requirements than past approaches. We also evaluate the challenges of applying these methods to Uyghur, a low-resource language.

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Contextual Parameter Generation for Universal Neural Machine Translation
Emmanouil Antonios Platanios | Mrinmaya Sachan | Graham Neubig | Tom Mitchell
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We propose a simple modification to existing neural machine translation (NMT) models that enables using a single universal model to translate between multiple languages while allowing for language specific parameterization, and that can also be used for domain adaptation. Our approach requires no changes to the model architecture of a standard NMT system, but instead introduces a new component, the contextual parameter generator (CPG), that generates the parameters of the system (e.g., weights in a neural network). This parameter generator accepts source and target language embeddings as input, and generates the parameters for the encoder and the decoder, respectively. The rest of the model remains unchanged and is shared across all languages. We show how this simple modification enables the system to use monolingual data for training and also perform zero-shot translation. We further show it is able to surpass state-of-the-art performance for both the IWSLT-15 and IWSLT-17 datasets and that the learned language embeddings are able to uncover interesting relationships between languages.

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MTNT: A Testbed for Machine Translation of Noisy Text
Paul Michel | Graham Neubig
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Noisy or non-standard input text can cause disastrous mistranslations in most modern Machine Translation (MT) systems, and there has been growing research interest in creating noise-robust MT systems. However, as of yet there are no publicly available parallel corpora of with naturally occurring noisy inputs and translations, and thus previous work has resorted to evaluating on synthetically created datasets. In this paper, we propose a benchmark dataset for Machine Translation of Noisy Text (MTNT), consisting of noisy comments on Reddit (www.reddit.com) and professionally sourced translations. We commissioned translations of English comments into French and Japanese, as well as French and Japanese comments into English, on the order of 7k-37k sentences per language pair. We qualitatively and quantitatively examine the types of noise included in this dataset, then demonstrate that existing MT models fail badly on a number of noise-related phenomena, even after performing adaptation on a small training set of in-domain data. This indicates that this dataset can provide an attractive testbed for methods tailored to handling noisy text in MT.

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SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation
Xinyi Wang | Hieu Pham | Zihang Dai | Graham Neubig
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

In this work, we examine methods for data augmentation for text-based tasks such as neural machine translation (NMT). We formulate the design of a data augmentation policy with desirable properties as an optimization problem, and derive a generic analytic solution. This solution not only subsumes some existing augmentation schemes, but also leads to an extremely simple data augmentation strategy for NMT: randomly replacing words in both the source sentence and the target sentence with other random words from their corresponding vocabularies. We name this method SwitchOut. Experiments on three translation datasets of different scales show that SwitchOut yields consistent improvements of about 0.5 BLEU, achieving better or comparable performances to strong alternatives such as word dropout (Sennrich et al., 2016a). Code to implement this method is included in the appendix.

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Rapid Adaptation of Neural Machine Translation to New Languages
Graham Neubig | Junjie Hu
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

This paper examines the problem of adapting neural machine translation systems to new, low-resourced languages (LRLs) as effectively and rapidly as possible. We propose methods based on starting with massively multilingual “seed models”, which can be trained ahead-of-time, and then continuing training on data related to the LRL. We contrast a number of strategies, leading to a novel, simple, yet effective method of “similar-language regularization”, where we jointly train on both a LRL of interest and a similar high-resourced language to prevent over-fitting to small LRL data. Experiments demonstrate that massively multilingual models, even without any explicit adaptation, are surprisingly effective, achieving BLEU scores of up to 15.5 with no data from the LRL, and that the proposed similar-language regularization method improves over other adaptation methods by 1.7 BLEU points average over 4 LRL settings.

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Retrieval-Based Neural Code Generation
Shirley Anugrah Hayati | Raphael Olivier | Pravalika Avvaru | Pengcheng Yin | Anthony Tomasic | Graham Neubig
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

In models to generate program source code from natural language, representing this code in a tree structure has been a common approach. However, existing methods often fail to generate complex code correctly due to a lack of ability to memorize large and complex structures. We introduce RECODE, a method based on subtree retrieval that makes it possible to explicitly reference existing code examples within a neural code generation model. First, we retrieve sentences that are similar to input sentences using a dynamic-programming-based sentence similarity scoring method. Next, we extract n-grams of action sequences that build the associated abstract syntax tree. Finally, we increase the probability of actions that cause the retrieved n-gram action subtree to be in the predicted code. We show that our approach improves the performance on two code generation tasks by up to +2.6 BLEU.

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Unsupervised Learning of Syntactic Structure with Invertible Neural Projections
Junxian He | Graham Neubig | Taylor Berg-Kirkpatrick
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Unsupervised learning of syntactic structure is typically performed using generative models with discrete latent variables and multinomial parameters. In most cases, these models have not leveraged continuous word representations. In this work, we propose a novel generative model that jointly learns discrete syntactic structure and continuous word representations in an unsupervised fashion by cascading an invertible neural network with a structured generative prior. We show that the invertibility condition allows for efficient exact inference and marginal likelihood computation in our model so long as the prior is well-behaved. In experiments we instantiate our approach with both Markov and tree-structured priors, evaluating on two tasks: part-of-speech (POS) induction, and unsupervised dependency parsing without gold POS annotation. On the Penn Treebank, our Markov-structured model surpasses state-of-the-art results on POS induction. Similarly, we find that our tree-structured model achieves state-of-the-art performance on unsupervised dependency parsing for the difficult training condition where neither gold POS annotation nor punctuation-based constraints are available.

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Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations
Aditi Chaudhary | Chunting Zhou | Lori Levin | Graham Neubig | David R. Mortensen | Jaime Carbonell
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Much work in Natural Language Processing (NLP) has been for resource-rich languages, making generalization to new, less-resourced languages challenging. We present two approaches for improving generalization to low-resourced languages by adapting continuous word representations using linguistically motivated subword units: phonemes, morphemes and graphemes. Our method requires neither parallel corpora nor bilingual dictionaries and provides a significant gain in performance over previous methods relying on these resources. We demonstrate the effectiveness of our approaches on Named Entity Recognition for four languages, namely Uyghur, Turkish, Bengali and Hindi, of which Uyghur and Bengali are low resource languages, and also perform experiments on Machine Translation. Exploiting subwords with transfer learning gives us a boost of +15.2 NER F1 for Uyghur and +9.7 F1 for Bengali. We also show improvements in the monolingual setting where we achieve (avg.) +3 F1 and (avg.) +1.35 BLEU.

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A Tree-based Decoder for Neural Machine Translation
Xinyi Wang | Hieu Pham | Pengcheng Yin | Graham Neubig
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Recent advances in Neural Machine Translation (NMT) show that adding syntactic information to NMT systems can improve the quality of their translations. Most existing work utilizes some specific types of linguistically-inspired tree structures, like constituency and dependency parse trees. This is often done via a standard RNN decoder that operates on a linearized target tree structure. However, it is an open question of what specific linguistic formalism, if any, is the best structural representation for NMT. In this paper, we (1) propose an NMT model that can naturally generate the topology of an arbitrary tree structure on the target side, and (2) experiment with various target tree structures. Our experiments show the surprising result that our model delivers the best improvements with balanced binary trees constructed without any linguistic knowledge; this model outperforms standard seq2seq models by up to 2.1 BLEU points, and other methods for incorporating target-side syntax by up to 0.7 BLEU.

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TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation
Pengcheng Yin | Graham Neubig
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We present TRANX, a transition-based neural semantic parser that maps natural language (NL) utterances into formal meaning representations (MRs). TRANX uses a transition system based on the abstract syntax description language for the target MR, which gives it two major advantages: (1) it is highly accurate, using information from the syntax of the target MR to constrain the output space and model the information flow, and (2) it is highly generalizable, and can easily be applied to new types of MR by just writing a new abstract syntax description corresponding to the allowable structures in the MR. Experiments on four different semantic parsing and code generation tasks show that our system is generalizable, extensible, and effective, registering strong results compared to existing neural semantic parsers.

2017

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Morphological Inflection Generation with Multi-space Variational Encoder-Decoders
Chunting Zhou | Graham Neubig
Proceedings of the CoNLL SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection

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Phonemic Transcription of Low-Resource Tonal Languages
Oliver Adams | Trevor Cohn | Graham Neubig | Alexis Michaud
Proceedings of the Australasian Language Technology Association Workshop 2017

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Improving Neural Machine Translation through Phrase-based Forced Decoding
Jingyi Zhang | Masao Utiyama | Eiichro Sumita | Graham Neubig | Satoshi Nakamura
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Compared to traditional statistical machine translation (SMT), neural machine translation (NMT) often sacrifices adequacy for the sake of fluency. We propose a method to combine the advantages of traditional SMT and NMT by exploiting an existing phrase-based SMT model to compute the phrase-based decoding cost for an NMT output and then using the phrase-based decoding cost to rerank the n-best NMT outputs. The main challenge in implementing this approach is that NMT outputs may not be in the search space of the standard phrase-based decoding algorithm, because the search space of phrase-based SMT is limited by the phrase-based translation rule table. We propose a soft forced decoding algorithm, which can always successfully find a decoding path for any NMT output. We show that using the forced decoding cost to rerank the NMT outputs can successfully improve translation quality on four different language pairs.

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Neural Lattice-to-Sequence Models for Uncertain Inputs
Matthias Sperber | Graham Neubig | Jan Niehues | Alex Waibel
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

The input to a neural sequence-to-sequence model is often determined by an up-stream system, e.g. a word segmenter, part of speech tagger, or speech recognizer. These up-stream models are potentially error-prone. Representing inputs through word lattices allows making this uncertainty explicit by capturing alternative sequences and their posterior probabilities in a compact form. In this work, we extend the TreeLSTM (Tai et al., 2015) into a LatticeLSTM that is able to consume word lattices, and can be used as encoder in an attentional encoder-decoder model. We integrate lattice posterior scores into this architecture by extending the TreeLSTM’s child-sum and forget gates and introducing a bias term into the attention mechanism. We experiment with speech translation lattices and report consistent improvements over baselines that translate either the 1-best hypothesis or the lattice without posterior scores.

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Learning Language Representations for Typology Prediction
Chaitanya Malaviya | Graham Neubig | Patrick Littell
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

One central mystery of neural NLP is what neural models “know” about their subject matter. When a neural machine translation system learns to translate from one language to another, does it learn the syntax or semantics of the languages? Can this knowledge be extracted from the system to fill holes in human scientific knowledge? Existing typological databases contain relatively full feature specifications for only a few hundred languages. Exploiting the existence of parallel texts in more than a thousand languages, we build a massive many-to-one NMT system from 1017 languages into English, and use this to predict information missing from typological databases. Experiments show that the proposed method is able to infer not only syntactic, but also phonological and phonetic inventory features, and improves over a baseline that has access to information about the languages geographic and phylogenetic neighbors.

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Charmanteau: Character Embedding Models For Portmanteau Creation
Varun Gangal | Harsh Jhamtani | Graham Neubig | Eduard Hovy | Eric Nyberg
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Portmanteaus are a word formation phenomenon where two words combine into a new word. We propose character-level neural sequence-to-sequence (S2S) methods for the task of portmanteau generation that are end-to-end-trainable, language independent, and do not explicitly use additional phonetic information. We propose a noisy-channel-style model, which allows for the incorporation of unsupervised word lists, improving performance over a standard source-to-target model. This model is made possible by an exhaustive candidate generation strategy specifically enabled by the features of the portmanteau task. Experiments find our approach superior to a state-of-the-art FST-based baseline with respect to ground truth accuracy and human evaluation.

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Cross-Lingual Word Embeddings for Low-Resource Language Modeling
Oliver Adams | Adam Makarucha | Graham Neubig | Steven Bird | Trevor Cohn
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Most languages have no established writing system and minimal written records. However, textual data is essential for natural language processing, and particularly important for training language models to support speech recognition. Even in cases where text data is missing, there are some languages for which bilingual lexicons are available, since creating lexicons is a fundamental task of documentary linguistics. We investigate the use of such lexicons to improve language models when textual training data is limited to as few as a thousand sentences. The method involves learning cross-lingual word embeddings as a preliminary step in training monolingual language models. Results across a number of languages show that language models are improved by this pre-training. Application to Yongning Na, a threatened language, highlights challenges in deploying the approach in real low-resource environments.

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Learning to Translate in Real-time with Neural Machine Translation
Jiatao Gu | Graham Neubig | Kyunghyun Cho | Victor O.K. Li
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Translating in real-time, a.k.a.simultaneous translation, outputs translation words before the input sentence ends, which is a challenging problem for conventional machine translation methods. We propose a neural machine translation (NMT) framework for simultaneous translation in which an agent learns to make decisions on when to translate from the interaction with a pre-trained NMT environment. To trade off quality and delay, we extensively explore various targets for delay and design a method for beam-search applicable in the simultaneous MT setting. Experiments against state-of-the-art baselines on two language pairs demonstrate the efficacy of the proposed framework both quantitatively and qualitatively.

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What Do Recurrent Neural Network Grammars Learn About Syntax?
Adhiguna Kuncoro | Miguel Ballesteros | Lingpeng Kong | Chris Dyer | Graham Neubig | Noah A. Smith
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Recurrent neural network grammars (RNNG) are a recently proposed probablistic generative modeling family for natural language. They show state-of-the-art language modeling and parsing performance. We investigate what information they learn, from a linguistic perspective, through various ablations to the model and the data, and by augmenting the model with an attention mechanism (GA-RNNG) to enable closer inspection. We find that explicit modeling of composition is crucial for achieving the best performance. Through the attention mechanism, we find that headedness plays a central role in phrasal representation (with the model’s latent attention largely agreeing with predictions made by hand-crafted head rules, albeit with some important differences). By training grammars without nonterminal labels, we find that phrasal representations depend minimally on nonterminals, providing support for the endocentricity hypothesis.

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Proceedings of the First Workshop on Neural Machine Translation
Thang Luong | Alexandra Birch | Graham Neubig | Andrew Finch
Proceedings of the First Workshop on Neural Machine Translation

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Stronger Baselines for Trustable Results in Neural Machine Translation
Michael Denkowski | Graham Neubig
Proceedings of the First Workshop on Neural Machine Translation

Interest in neural machine translation has grown rapidly as its effectiveness has been demonstrated across language and data scenarios. New research regularly introduces architectural and algorithmic improvements that lead to significant gains over “vanilla” NMT implementations. However, these new techniques are rarely evaluated in the context of previously published techniques, specifically those that are widely used in state-of-the-art production and shared-task systems. As a result, it is often difficult to determine whether improvements from research will carry over to systems deployed for real-world use. In this work, we recommend three specific methods that are relatively easy to implement and result in much stronger experimental systems. Beyond reporting significantly higher BLEU scores, we conduct an in-depth analysis of where improvements originate and what inherent weaknesses of basic NMT models are being addressed. We then compare the relative gains afforded by several other techniques proposed in the literature when starting with vanilla systems versus our stronger baselines, showing that experimental conclusions may change depending on the baseline chosen. This indicates that choosing a strong baseline is crucial for reporting reliable experimental results.

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An Empirical Study of Mini-Batch Creation Strategies for Neural Machine Translation
Makoto Morishita | Yusuke Oda | Graham Neubig | Koichiro Yoshino | Katsuhito Sudoh | Satoshi Nakamura
Proceedings of the First Workshop on Neural Machine Translation

Training of neural machine translation (NMT) models usually uses mini-batches for efficiency purposes. During the mini-batched training process, it is necessary to pad shorter sentences in a mini-batch to be equal in length to the longest sentence therein for efficient computation. Previous work has noted that sorting the corpus based on the sentence length before making mini-batches reduces the amount of padding and increases the processing speed. However, despite the fact that mini-batch creation is an essential step in NMT training, widely used NMT toolkits implement disparate strategies for doing so, which have not been empirically validated or compared. This work investigates mini-batch creation strategies with experiments over two different datasets. Our results suggest that the choice of a mini-batch creation strategy has a large effect on NMT training and some length-based sorting strategies do not always work well compared with simple shuffling.

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Tree as a Pivot: Syntactic Matching Methods in Pivot Translation
Akiva Miura | Graham Neubig | Katsuhito Sudoh | Satoshi Nakamura
Proceedings of the Second Conference on Machine Translation

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NICT-NAIST System for WMT17 Multimodal Translation Task
Jingyi Zhang | Masao Utiyama | Eiichro Sumita | Graham Neubig | Satoshi Nakamura
Proceedings of the Second Conference on Machine Translation

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How Would You Say It? Eliciting Lexically Diverse Dialogue for Supervised Semantic Parsing
Abhilasha Ravichander | Thomas Manzini | Matthias Grabmair | Graham Neubig | Jonathan Francis | Eric Nyberg
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

Building dialogue interfaces for real-world scenarios often entails training semantic parsers starting from zero examples. How can we build datasets that better capture the variety of ways users might phrase their queries, and what queries are actually realistic? Wang et al. (2015) proposed a method to build semantic parsing datasets by generating canonical utterances using a grammar and having crowdworkers paraphrase them into natural wording. A limitation of this approach is that it induces bias towards using similar language as the canonical utterances. In this work, we present a methodology that elicits meaningful and lexically diverse queries from users for semantic parsing tasks. Starting from a seed lexicon and a generative grammar, we pair logical forms with mixed text-image representations and ask crowdworkers to paraphrase and confirm the plausibility of the queries that they generated. We use this method to build a semantic parsing dataset from scratch for a dialog agent in a smart-home simulation. We find evidence that this dataset, which we have named SmartHome, is demonstrably more lexically diverse and difficult to parse than existing domain-specific semantic parsing datasets.

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Overview of the 4th Workshop on Asian Translation
Toshiaki Nakazawa | Shohei Higashiyama | Chenchen Ding | Hideya Mino | Isao Goto | Hideto Kazawa | Yusuke Oda | Graham Neubig | Sadao Kurohashi
Proceedings of the 4th Workshop on Asian Translation (WAT2017)

This paper presents the results of the shared tasks from the 4th workshop on Asian translation (WAT2017) including J↔E, J↔C scientific paper translation subtasks, C↔J, K↔J, E↔J patent translation subtasks, H↔E mixed domain subtasks, J↔E newswire subtasks and J↔E recipe subtasks. For the WAT2017, 12 institutions participated in the shared tasks. About 300 translation results have been submitted to the automatic evaluation server, and selected submissions were manually evaluated.

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Multi-space Variational Encoder-Decoders for Semi-supervised Labeled Sequence Transduction
Chunting Zhou | Graham Neubig
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Labeled sequence transduction is a task of transforming one sequence into another sequence that satisfies desiderata specified by a set of labels. In this paper we propose multi-space variational encoder-decoders, a new model for labeled sequence transduction with semi-supervised learning. The generative model can use neural networks to handle both discrete and continuous latent variables to exploit various features of data. Experiments show that our model provides not only a powerful supervised framework but also can effectively take advantage of the unlabeled data. On the SIGMORPHON morphological inflection benchmark, our model outperforms single-model state-of-art results by a large margin for the majority of languages.

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A Syntactic Neural Model for General-Purpose Code Generation
Pengcheng Yin | Graham Neubig
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We consider the problem of parsing natural language descriptions into source code written in a general-purpose programming language like Python. Existing data-driven methods treat this problem as a language generation task without considering the underlying syntax of the target programming language. Informed by previous work in semantic parsing, in this paper we propose a novel neural architecture powered by a grammar model to explicitly capture the target syntax as prior knowledge. Experiments find this an effective way to scale up to generation of complex programs from natural language descriptions, achieving state-of-the-art results that well outperform previous code generation and semantic parsing approaches.

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Neural Machine Translation via Binary Code Prediction
Yusuke Oda | Philip Arthur | Graham Neubig | Koichiro Yoshino | Satoshi Nakamura
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In this paper, we propose a new method for calculating the output layer in neural machine translation systems. The method is based on predicting a binary code for each word and can reduce computation time/memory requirements of the output layer to be logarithmic in vocabulary size in the best case. In addition, we also introduce two advanced approaches to improve the robustness of the proposed model: using error-correcting codes and combining softmax and binary codes. Experiments on two English-Japanese bidirectional translation tasks show proposed models achieve BLEU scores that approach the softmax, while reducing memory usage to the order of less than 1/10 and improving decoding speed on CPUs by x5 to x10.

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Learning Character-level Compositionality with Visual Features
Frederick Liu | Han Lu | Chieh Lo | Graham Neubig
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Previous work has modeled the compositionality of words by creating character-level models of meaning, reducing problems of sparsity for rare words. However, in many writing systems compositionality has an effect even on the character-level: the meaning of a character is derived by the sum of its parts. In this paper, we model this effect by creating embeddings for characters based on their visual characteristics, creating an image for the character and running it through a convolutional neural network to produce a visual character embedding. Experiments on a text classification task demonstrate that such model allows for better processing of instances with rare characters in languages such as Chinese, Japanese, and Korean. Additionally, qualitative analyses demonstrate that our proposed model learns to focus on the parts of characters that carry topical content which resulting in embeddings that are coherent in visual space.

2016

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Generalizing and Hybridizing Count-based and Neural Language Models
Graham Neubig | Chris Dyer
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Controlling Output Length in Neural Encoder-Decoders
Yuta Kikuchi | Graham Neubig | Ryohei Sasano | Hiroya Takamura | Manabu Okumura
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Incorporating Discrete Translation Lexicons into Neural Machine Translation
Philip Arthur | Graham Neubig | Satoshi Nakamura
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Learning a Lexicon and Translation Model from Phoneme Lattices
Oliver Adams | Graham Neubig | Trevor Cohn | Steven Bird | Quoc Truong Do | Satoshi Nakamura
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Practical Neural Networks for NLP: From Theory to Code
Chris Dyer | Yoav Goldberg | Graham Neubig
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts

This tutorial aims to bring NLP researchers up to speed with the current techniques in deep learning and neural networks, and show them how they can turn their ideas into practical implementations. We will start with simple classification models (logistic regression and multilayer perceptrons) and cover more advanced patterns that come up in NLP such as recurrent networks for sequence tagging and prediction problems, structured networks (e.g., compositional architectures based on syntax trees), structured output spaces (sequences and trees), attention for sequence-to-sequence transduction, and feature induction for complex algorithm states. A particular emphasis will be on learning to represent complex objects as recursive compositions of simpler objects. This representation will reflect characterize standard objects in NLP, such as the composition of characters and morphemes into words, and words into sentences and documents. In addition, new opportunities such as learning to embed "algorithm states" such as those used in transition-based parsing and other sequential structured prediction models (for which effective features may be difficult to engineer by hand) will be covered.Everything in the tutorial will be grounded in code — we will show how to program seemingly complex neural-net models using toolkits based on the computation-graph formalism. Computation graphs decompose complex computations into a DAG, with nodes representing inputs, target outputs, parameters, or (sub)differentiable functions (e.g., "tanh", "matrix multiply", and "softmax"), and edges represent data dependencies. These graphs can be run "forward" to make predictions and compute errors (e.g., log loss, squared error) and then "backward" to compute derivatives with respect to model parameters. In particular we'll cover the Python bindings of the CNN library. CNN has been designed from the ground up for NLP applications, dynamically structured NNs, rapid prototyping, and a transparent data and execution model.

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Selecting Syntactic, Non-redundant Segments in Active Learning for Machine Translation
Akiva Miura | Graham Neubig | Michael Paul | Satoshi Nakamura
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Morphological Inflection Generation Using Character Sequence to Sequence Learning
Manaal Faruqui | Yulia Tsvetkov | Graham Neubig | Chris Dyer
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Analyzing the Effect of Entrainment on Dialogue Acts
Masahiro Mizukami | Koichiro Yoshino | Graham Neubig | David Traum | Satoshi Nakamura
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Proceedings of the 3rd Workshop on Asian Translation (WAT2016)
Toshiaki Nakazawa | Hideya Mino | Chenchen Ding | Isao Goto | Graham Neubig | Sadao Kurohashi | Ir. Hammam Riza | Pushpak Bhattacharyya
Proceedings of the 3rd Workshop on Asian Translation (WAT2016)

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Overview of the 3rd Workshop on Asian Translation
Toshiaki Nakazawa | Chenchen Ding | Hideya Mino | Isao Goto | Graham Neubig | Sadao Kurohashi
Proceedings of the 3rd Workshop on Asian Translation (WAT2016)

This paper presents the results of the shared tasks from the 3rd workshop on Asian translation (WAT2016) including J ↔ E, J ↔ C scientific paper translation subtasks, C ↔ J, K ↔ J, E ↔ J patent translation subtasks, I ↔ E newswire subtasks and H ↔ E, H ↔ J mixed domain subtasks. For the WAT2016, 15 institutions participated in the shared tasks. About 500 translation results have been submitted to the automatic evaluation server, and selected submissions were manually evaluated.

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Lexicons and Minimum Risk Training for Neural Machine Translation: NAIST-CMU at WAT2016
Graham Neubig
Proceedings of the 3rd Workshop on Asian Translation (WAT2016)

This year, the Nara Institute of Science and Technology (NAIST)/Carnegie Mellon University (CMU) submission to the Japanese-English translation track of the 2016 Workshop on Asian Translation was based on attentional neural machine translation (NMT) models. In addition to the standard NMT model, we make a number of improvements, most notably the use of discrete translation lexicons to improve probability estimates, and the use of minimum risk training to optimize the MT system for BLEU score. As a result, our system achieved the highest translation evaluation scores for the task.

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Optimizing Computer-Assisted Transcription Quality with Iterative User Interfaces
Matthias Sperber | Graham Neubig | Satoshi Nakamura | Alex Waibel
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Computer-assisted transcription promises high-quality speech transcription at reduced costs. This is achieved by limiting human effort to transcribing parts for which automatic transcription quality is insufficient. Our goal is to improve the human transcription quality via appropriate user interface design. We focus on iterative interfaces that allow humans to solve tasks based on an initially given suggestion, in this case an automatic transcription. We conduct a user study that reveals considerable quality gains for three variations of iterative interfaces over a non-iterative from-scratch transcription interface. Our iterative interfaces included post-editing, confidence-enhanced post-editing, and a novel retyping interface. All three yielded similar quality on average, but we found that the proposed retyping interface was less sensitive to the difficulty of the segment, and superior when the automatic transcription of the segment contained relatively many errors. An analysis using mixed-effects models allows us to quantify these and other factors and draw conclusions over which interface design should be chosen in which circumstance.

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Lightly Supervised Quality Estimation
Matthias Sperber | Graham Neubig | Jan Niehues | Sebastian Stüker | Alex Waibel
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Evaluating the quality of output from language processing systems such as machine translation or speech recognition is an essential step in ensuring that they are sufficient for practical use. However, depending on the practical requirements, evaluation approaches can differ strongly. Often, reference-based evaluation measures (such as BLEU or WER) are appealing because they are cheap and allow rapid quantitative comparison. On the other hand, practitioners often focus on manual evaluation because they must deal with frequently changing domains and quality standards requested by customers, for which reference-based evaluation is insufficient or not possible due to missing in-domain reference data (Harris et al., 2016). In this paper, we attempt to bridge this gap by proposing a framework for lightly supervised quality estimation. We collect manually annotated scores for a small number of segments in a test corpus or document, and combine them with automatically predicted quality scores for the remaining segments to predict an overall quality estimate. An evaluation shows that our framework estimates quality more reliably than using fully automatic quality estimation approaches, while keeping annotation effort low by not requiring full references to be available for the particular domain.

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Optimization for Statistical Machine Translation: A Survey
Graham Neubig | Taro Watanabe
Computational Linguistics, Volume 42, Issue 1 - March 2016

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A Continuous Space Rule Selection Model for Syntax-based Statistical Machine Translation
Jingyi Zhang | Masao Utiyama | Eiichro Sumita | Graham Neubig | Satoshi Nakamura
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

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Multi-Target Machine Translation with Multi-Synchronous Context-free Grammars
Graham Neubig | Philip Arthur | Kevin Duh
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Ckylark: A More Robust PCFG-LA Parser
Yusuke Oda | Graham Neubig | Sakriani Sakti | Tomoki Toda | Satoshi Nakamura
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

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A Binarized Neural Network Joint Model for Machine Translation
Jingyi Zhang | Masao Utiyama | Eiichiro Sumita | Graham Neubig | Satoshi Nakamura
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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An Investigation of Machine Translation Evaluation Metrics in Cross-lingual Question Answering
Kyoshiro Sugiyama | Masahiro Mizukami | Graham Neubig | Koichiro Yoshino | Sakriani Sakti | Tomoki Toda | Satoshi Nakamura
Proceedings of the Tenth Workshop on Statistical Machine Translation

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Proceedings of the 2nd Workshop on Asian Translation (WAT2015)
Toshiaki Nakazawa | Hideya Mino | Isao Goto | Graham Neubig | Sadao Kurohashi | Eiichiro Sumita
Proceedings of the 2nd Workshop on Asian Translation (WAT2015)

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Overview of the 2nd Workshop on Asian Translation
Toshiaki Nakazawa | Hideya Mino | Isao Goto | Graham Neubig | Sadao Kurohashi | Eiichiro Sumita
Proceedings of the 2nd Workshop on Asian Translation (WAT2015)

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Neural Reranking Improves Subjective Quality of Machine Translation: NAIST at WAT2015
Graham Neubig | Makoto Morishita | Satoshi Nakamura
Proceedings of the 2nd Workshop on Asian Translation (WAT2015)

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Syntax-based Simultaneous Translation through Prediction of Unseen Syntactic Constituents
Yusuke Oda | Graham Neubig | Sakriani Sakti | Tomoki Toda | Satoshi Nakamura
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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Improving Pivot Translation by Remembering the Pivot
Akiva Miura | Graham Neubig | Sakriani Sakti | Tomoki Toda | Satoshi Nakamura
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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Semantic Parsing of Ambiguous Input through Paraphrasing and Verification
Philip Arthur | Graham Neubig | Sakriani Sakti | Tomoki Toda | Satoshi Nakamura
Transactions of the Association for Computational Linguistics, Volume 3

We propose a new method for semantic parsing of ambiguous and ungrammatical input, such as search queries. We do so by building on an existing semantic parsing framework that uses synchronous context free grammars (SCFG) to jointly model the input sentence and output meaning representation. We generalize this SCFG framework to allow not one, but multiple outputs. Using this formalism, we construct a grammar that takes an ambiguous input string and jointly maps it into both a meaning representation and a natural language paraphrase that is less ambiguous than the original input. This paraphrase can be used to disambiguate the meaning representation via verification using a language model that calculates the probability of each paraphrase.

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The NAIST English speech recognition system for IWSLT 2015
Michael Heck | Quoc Truong Do | Sakriani Sakti | Graham Neubig | Satoshi Nakamura
Proceedings of the 12th International Workshop on Spoken Language Translation: Evaluation Campaign

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Improving translation of emphasis with pause prediction in speech-to-speech translation systems
Quoc Truong Do | Sakriani Sakti | Graham Neubig | Tomoki Toda | Satoshi Nakamura
Proceedings of the 12th International Workshop on Spoken Language Translation: Papers

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Parser self-training for syntax-based machine translation
Makoto Morishita | Koichi Akabe | Yuto Hatakoshi | Graham Neubig | Koichiro Yoshino | Satoshi Nakamura
Proceedings of the 12th International Workshop on Spoken Language Translation: Papers

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Inducing bilingual lexicons from small quantities of sentence-aligned phonemic transcriptions
Oliver Adams | Graham Neubig | Trevor Cohn | Steven Bird
Proceedings of the 12th International Workshop on Spoken Language Translation: Papers

2014

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NTT-NAIST syntax-based SMT systems for IWSLT 2014
Katsuhito Sudoh | Graham Neubig | Kevin Duh | Katsuhiko Hayashi
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper presents NTT-NAIST SMT systems for English-German and German-English MT tasks of the IWSLT 2014 evaluation campaign. The systems are based on generalized minimum Bayes risk system combination of three SMT systems using the forest-to-string, syntactic preordering, and phrase-based translation formalisms. Individual systems employ training data selection for domain adaptation, truecasing, compound word splitting (for GermanEnglish), interpolated n-gram language models, and hypotheses rescoring using recurrent neural network language models.

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The NAIST-NTT TED talk treebank
Graham Neubig | Katsuhiro Sudoh | Yusuke Oda | Kevin Duh | Hajime Tsukuda | Masaaki Nagata
Proceedings of the 11th International Workshop on Spoken Language Translation: Papers

Syntactic parsing is a fundamental natural language processing technology that has proven useful in machine translation, language modeling, sentence segmentation, and a number of other applications related to speech translation. However, there is a paucity of manually annotated syntactic parsing resources for speech, and particularly for the lecture speech that is the current target of the IWSLT translation campaign. In this work, we present a new manually annotated treebank of TED talks that we hope will prove useful for investigation into the interaction between syntax and these speechrelated applications. The first version of the corpus includes 1,217 sentences and 23,158 words manually annotated with parse trees, and aligned with translations in 26-43 different languages. In this paper we describe the collection of the corpus, and an analysis of its various characteristics.

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Collection of a Simultaneous Translation Corpus for Comparative Analysis
Hiroaki Shimizu | Graham Neubig | Sakriani Sakti | Tomoki Toda | Satoshi Nakamura
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper describes the collection of an English-Japanese/Japanese-English simultaneous interpretation corpus. There are two main features of the corpus. The first is that professional simultaneous interpreters with different amounts of experience cooperated with the collection. By comparing data from simultaneous interpretation of each interpreter, it is possible to compare better interpretations to those that are not as good. The second is that for part of our corpus there are already translation data available. This makes it possible to compare translation data with simultaneous interpretation data. We recorded the interpretations of lectures and news, and created time-aligned transcriptions. A total of 387k words of transcribed data were collected. The corpus will be helpful to analyze differences in interpretations styles and to construct simultaneous interpretation systems.

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Language Resource Addition: Dictionary or Corpus?
Shinsuke Mori | Graham Neubig
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper, we investigate the relative effect of two strategies of language resource additions to the word segmentation problem and part-of-speech tagging problem in Japanese. The first strategy is adding entries to the dictionary and the second is adding annotated sentences to the training corpus. The experimental results showed that the annotated sentence addition to the training corpus is better than the entries addition to the dictionary. And the annotated sentence addition is efficient especially when we add new words with contexts of three real occurrences as partially annotated sentences. According to this knowledge, we executed annotation on the invention disclosure texts and observed word segmentation accuracy.

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Towards Multilingual Conversations in the Medical Domain: Development of Multilingual Medical Data and A Network-based ASR System
Sakriani Sakti | Keigo Kubo | Sho Matsumiya | Graham Neubig | Tomoki Toda | Satoshi Nakamura | Fumihiro Adachi | Ryosuke Isotani
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper outlines the recent development on multilingual medical data and multilingual speech recognition system for network-based speech-to-speech translation in the medical domain. The overall speech-to-speech translation (S2ST) system was designed to translate spoken utterances from a given source language into a target language in order to facilitate multilingual conversations and reduce the problems caused by language barriers in medical situations. Our final system utilizes a weighted finite-state transducers with n-gram language models. Currently, the system successfully covers three languages: Japanese, English, and Chinese. The difficulties involved in connecting Japanese, English and Chinese speech recognition systems through Web servers will be discussed, and the experimental results in simulated medical conversation will also be presented.

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Linguistic and Acoustic Features for Automatic Identification of Autism Spectrum Disorders in Children’s Narrative
Hiroki Tanaka | Sakriani Sakti | Graham Neubig | Tomoki Toda | Satoshi Nakamura
Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality

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Rule-based Syntactic Preprocessing for Syntax-based Machine Translation
Yuto Hatakoshi | Graham Neubig | Sakriani Sakti | Tomoki Toda | Satoshi Nakamura
Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation

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Forest-to-String SMT for Asian Language Translation: NAIST at WAT 2014
Graham Neubig
Proceedings of the 1st Workshop on Asian Translation (WAT2014)

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Segmentation for Efficient Supervised Language Annotation with an Explicit Cost-Utility Tradeoff
Matthias Sperber | Mirjam Simantzik | Graham Neubig | Satoshi Nakamura | Alex Waibel
Transactions of the Association for Computational Linguistics, Volume 2

In this paper, we study the problem of manually correcting automatic annotations of natural language in as efficient a manner as possible. We introduce a method for automatically segmenting a corpus into chunks such that many uncertain labels are grouped into the same chunk, while human supervision can be omitted altogether for other segments. A tradeoff must be found for segment sizes. Choosing short segments allows us to reduce the number of highly confident labels that are supervised by the annotator, which is useful because these labels are often already correct and supervising correct labels is a waste of effort. In contrast, long segments reduce the cognitive effort due to context switches. Our method helps find the segmentation that optimizes supervision efficiency by defining user models to predict the cost and utility of supervising each segment and solving a constrained optimization problem balancing these contradictory objectives. A user study demonstrates noticeable gains over pre-segmented, confidence-ordered baselines on two natural language processing tasks: speech transcription and word segmentation.

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On the Elements of an Accurate Tree-to-String Machine Translation System
Graham Neubig | Kevin Duh
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Optimizing Segmentation Strategies for Simultaneous Speech Translation
Yusuke Oda | Graham Neubig | Sakriani Sakti | Tomoki Toda | Satoshi Nakamura
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Discriminative Language Models as a Tool for Machine Translation Error Analysis
Koichi Akabe | Graham Neubig | Sakriani Sakti | Tomoki Toda | Satoshi Nakamura
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Reinforcement Learning of Cooperative Persuasive Dialogue Policies using Framing
Takuya Hiraoka | Graham Neubig | Sakriani Sakti | Tomoki Toda | Satoshi Nakamura
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Acquiring a Dictionary of Emotion-Provoking Events
Hoa Trong Vu | Graham Neubig | Sakriani Sakti | Tomoki Toda | Satoshi Nakamura
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers

2013

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Adaptation Data Selection using Neural Language Models: Experiments in Machine Translation
Kevin Duh | Graham Neubig | Katsuhito Sudoh | Hajime Tsukada
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Travatar: A Forest-to-String Machine Translation Engine based on Tree Transducers
Graham Neubig
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations

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Proceedings of the Workshop on Language Processing and Crisis Information 2013
Kentaro Inui | Hideto Kazawa | Graham Neubig | Masao Utiyama
Proceedings of the Workshop on Language Processing and Crisis Information 2013

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A Framework and Tool for Collaborative Extraction of Reliable Information
Graham Neubig | Shinsuke Mori | Masahiro Mizukami
Proceedings of the Workshop on Language Processing and Crisis Information 2013

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Towards High-Reliability Speech Translation in the Medical Domain
Graham Neubig | Sakriani Sakti | Tomoki Toda | Satoshi Nakamura | Yuji Matsumoto | Ryosuke Isotani | Yukichi Ikeda
The First Workshop on Natural Language Processing for Medical and Healthcare Fields

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NTT-NAIST SMT systems for IWSLT 2013
Katsuhito Sudoh | Graham Neubig | Kevin Duh | Hajime Tsukada
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper presents NTT-NAIST SMT systems for English-German and German-English MT tasks of the IWSLT 2013 evaluation campaign. The systems are based on generalized minimum Bayes risk system combination of three SMT systems: forest-to-string, hierarchical phrase-based, phrasebased with pre-ordering. Individual SMT systems include data selection for domain adaptation, rescoring using recurrent neural net language models, interpolated language models, and compound word splitting (only for German-English).

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The NAIST English speech recognition system for IWSLT 2013
Sakriani Sakti | Keigo Kubo | Graham Neubig | Tomoki Toda | Satoshi Nakamura
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes the NAIST English speech recognition system for the IWSLT 2013 Evaluation Campaign. In particular, we participated in the ASR track of the IWSLT TED task. Last year, we participated in collaboration with Karlsruhe Institute of Technology (KIT). This year is our first time to build a full-fledged ASR system for IWSLT solely developed by NAIST. Our final system utilizes weighted finitestate transducers with four-gram language models. The hypothesis selection is based on the principle of system combination. On the IWSLT official test set our system introduced in this work achieves a WER of 9.1% for tst2011, 10.0% for tst2012, and 16.2% for the new tst2013.

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Constructing a speech translation system using simultaneous interpretation data
Hiroaki Shimizu | Graham Neubig | Sakriani Sakti | Tomoki Toda | Satoshi Nakamura
Proceedings of the 10th International Workshop on Spoken Language Translation: Papers

There has been a fair amount of work on automatic speech translation systems that translate in real-time, serving as a computerized version of a simultaneous interpreter. It has been noticed in the field of translation studies that simultaneous interpreters perform a number of tricks to make the content easier to understand in real-time, including dividing their translations into small chunks, or summarizing less important content. However, the majority of previous work has not specifically considered this fact, simply using translation data (made by translators) for learning of the machine translation system. In this paper, we examine the possibilities of additionally incorporating simultaneous interpretation data (made by simultaneous interpreters) in the learning process. First we collect simultaneous interpretation data from professional simultaneous interpreters of three levels, and perform an analysis of the data. Next, we incorporate the simultaneous interpretation data in the learning of the machine translation system. As a result, the translation style of the system becomes more similar to that of a highly experienced simultaneous interpreter. We also find that according to automatic evaluation metrics, our system achieves performance similar to that of a simultaneous interpreter that has 1 year of experience.

2012

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The NAIST machine translation system for IWSLT2012
Graham Neubig | Kevin Duh | Masaya Ogushi | Takamoto Kano | Tetsuo Kiso | Sakriani Sakti | Tomoki Toda | Satoshi Nakamura
Proceedings of the 9th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes the NAIST statistical machine translation system for the IWSLT2012 Evaluation Campaign. We participated in all TED Talk tasks, for a total of 11 language-pairs. For all tasks, we use the Moses phrase-based decoder and its experiment management system as a common base for building translation systems. The focus of our work is on performing a comprehensive comparison of a multitude of existing techniques for the TED task, exploring issues such as out-of-domain data filtering, minimum Bayes risk decoding, MERT vs. PRO tuning, word alignment combination, and morphology.

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The 2012 KIT and KIT-NAIST English ASR systems for the IWSLT evaluation
Christian Saam | Christian Mohr | Kevin Kilgour | Michael Heck | Matthias Sperber | Keigo Kubo | Sebatian Stüker | Sakriani Sakri | Graham Neubig | Tomoki Toda | Satoshi Nakamura | Alex Waibel
Proceedings of the 9th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes our English Speech-to-Text (STT) systems for the 2012 IWSLT TED ASR track evaluation. The systems consist of 10 subsystems that are combinations of different front-ends, e.g. MVDR based and MFCC based ones, and two different phone sets. The outputs of the subsystems are combined via confusion network combination. Decoding is done in two stages, where the systems of the second stage are adapted in an unsupervised manner on the combination of the first stage outputs using VTLN, MLLR, and cM-LLR.

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The KIT-NAIST (contrastive) English ASR system for IWSLT 2012
Michael Heck | Keigo Kubo | Matthias Sperber | Sakriani Sakti | Sebastian Stüker | Christian Saam | Kevin Kilgour | Christian Mohr | Graham Neubig | Tomoki Toda | Satoshi Nakamura | Alex Waibel
Proceedings of the 9th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes the KIT-NAIST (Contrastive) English speech recognition system for the IWSLT 2012 Evaluation Campaign. In particular, we participated in the ASR track of the IWSLT TED task. The system was developed by Karlsruhe Institute of Technology (KIT) and Nara Institute of Science and Technology (NAIST) teams in collaboration within the interACT project. We employ single system decoding with fully continuous and semi-continuous models, as well as a three-stage, multipass system combination framework built with the Janus Recognition Toolkit. On the IWSLT 2010 test set our single system introduced in this work achieves a WER of 17.6%, and our final combination achieves a WER of 14.4%.

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A method for translation of paralinguistic information
Takatomo Kano | Sakriani Sakti | Shinnosuke Takamichi | Graham Neubig | Tomoki Toda | Satoshi Nakamura
Proceedings of the 9th International Workshop on Spoken Language Translation: Papers

This paper is concerned with speech-to-speech translation that is sensitive to paralinguistic information. From the many different possible paralinguistic features to handle, in this paper we chose duration and power as a first step, proposing a method that can translate these features from input speech to the output speech in continuous space. This is done in a simple and language-independent fashion by training a regression model that maps source language duration and power information into the target language. We evaluate the proposed method on a digit translation task and show that paralinguistic information in input speech appears in output speech, and that this information can be used by target language speakers to detect emphasis.

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Machine Translation without Words through Substring Alignment
Graham Neubig | Taro Watanabe | Shinsuke Mori | Tatsuya Kawahara
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Inducing a Discriminative Parser to Optimize Machine Translation Reordering
Graham Neubig | Taro Watanabe | Shinsuke Mori
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

2011

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The NICT translation system for IWSLT 2011
Andrew Finch | Chooi-Ling Goh | Graham Neubig | Eiichiro Sumita
Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes NICT’s participation in the IWSLT 2011 evaluation campaign for the TED speech translation ChineseEnglish shared-task. Our approach was based on a phrasebased statistical machine translation system that was augmented in two ways. Firstly we introduced rule-based re-ordering constraints on the decoding. This consisted of a set of rules that were used to segment the input utterances into segments that could be decoded almost independently. This idea here being that constraining the decoding process in this manner would greatly reduce the search space of the decoder, and cut out many possibilities for error while at the same time allowing for a correct output to be generated. The rules we used exploit punctuation and spacing in the input utterances, and we use these positions to delimit our segments. Not all punctuation/spacing positions were used as segment boundaries, and the set of used positions were determined by a set of linguistically-based heuristics. Secondly we used two heterogeneous methods to build the translation model, and lexical reordering model for our systems. The first method employed the popular method of using GIZA++ for alignment in combination with phraseextraction heuristics. The second method used a recentlydeveloped Bayesian alignment technique that is able to perform both phrase-to-phrase alignment and phrase pair extraction within a single unsupervised process. The models produced by this type of alignment technique are typically very compact whilst at the same time maintaining a high level of translation quality. We evaluated both of these methods of translation model construction in isolation, and our results show their performance is comparable. We also integrated both models by linear interpolation to obtain a model that outperforms either component. Finally, we added an indicator feature into the log-linear model to indicate those phrases that were in the intersection of the two translation models. The addition of this feature was also able to provide a small improvement in performance.

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An Unsupervised Model for Joint Phrase Alignment and Extraction
Graham Neubig | Taro Watanabe | Eiichiro Sumita | Shinsuke Mori | Tatsuya Kawahara
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Pointwise Prediction for Robust, Adaptable Japanese Morphological Analysis
Graham Neubig | Yosuke Nakata | Shinsuke Mori
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Training Dependency Parsers from Partially Annotated Corpora
Daniel Flannery | Yusuke Miayo | Graham Neubig | Shinsuke Mori
Proceedings of 5th International Joint Conference on Natural Language Processing

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Safety Information Mining — What can NLP do in a disaster—
Graham Neubig | Yuichiroh Matsubayashi | Masato Hagiwara | Koji Murakami
Proceedings of 5th International Joint Conference on Natural Language Processing

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Searching Translation Memories for Paraphrases
Masao Utiyama | Graham Neubig | Takashi Onishi | Eiichiro Sumita
Proceedings of Machine Translation Summit XIII: Papers

2010

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Word-based Partial Annotation for Efficient Corpus Construction
Graham Neubig | Shinsuke Mori
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

In order to utilize the corpus-based techniques that have proven effective in natural language processing in recent years, costly and time-consuming manual creation of linguistic resources is often necessary. Traditionally these resources are created on the document or sentence-level. In this paper, we examine the benefit of annotating only particular words with high information content, as opposed to the entire sentence or document. Using the task of Japanese pronunciation estimation as an example, we devise a machine learning method that can be trained on data annotated word-by-word. This is done by dividing the estimation process into two steps (word segmentation and word-based pronunciation estimation), and introducing a point-wise estimator that is able to make each decision independent of the other decisions made for a particular sentence. In an evaluation, the proposed strategy is shown to provide greater increases in accuracy using a smaller number of annotated words than traditional sentence-based annotation techniques.
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