Derry Tanti Wijaya

Also published as: Derry Wijaya


2024

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Could We Have Had Better Multilingual LLMs if English Was Not the Central Language?
Ryandito Diandaru | Lucky Susanto | Zilu Tang | Ayu Purwarianti | Derry Tanti Wijaya
Proceedings of the Second International Workshop Towards Digital Language Equality (TDLE): Focusing on Sustainability @ LREC-COLING 2024

Large Language Models (LLMs) demonstrate strong machine translation capabilities on languages they are trained on. However, the impact of factors beyond training data size on translation performance remains a topic of debate, especially concerning languages not directly encountered during training. Our study delves into Llama2’s translation capabilities. By modeling a linear relationship between linguistic feature distances and machine translation scores, we ask ourselves if there are potentially better central languages for LLMs other than English. Our experiments show that the 7B Llama2 model yields above 10 BLEU when translating into all languages it has seen, which rarely happens for languages it has not seen. Most translation improvements into unseen languages come from scaling up the model size rather than instruction tuning or increasing shot count. Furthermore, our correlation analysis reveals that syntactic similarity is not the only linguistic factor that strongly correlates with machine translation scores. Interestingly, we discovered that under specific circumstances, some languages (e.g. Swedish, Catalan), despite having significantly less training data, exhibit comparable correlation levels to English. These insights challenge the prevailing landscape of LLMs, suggesting that models centered around languages other than English could provide a more efficient foundation for multilingual applications.

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Generating Faithful and Salient Text from Multimodal Data
Tahsina Hashem | Weiqing Wang | Derry Tanti Wijaya | Mohammed Eunus Ali | Yuan-Fang Li
Proceedings of the 17th International Natural Language Generation Conference

While large multimodal models (LMMs) have obtained strong performance on many multimodal tasks, they may still hallucinate while generating text. Their performance on detecting salient features from visual data is also unclear. In this paper, we develop a framework to generate faithful and salient text from mixed-modal data, which includes images and structured data ( represented in knowledge graphs or tables). Specifically, we train a vision critic model to identify hallucinated and non-salient features from the image modality. The critic model also generates a list of salient image features. This information is used in the post editing step to improve the generation quality. Experiments on two datasets show that our framework improves LMMs’ generation quality on both faithfulness and saliency, outperforming recent techniques aimed at reducing hallucination. The dataset and code are available at https://github.com/TahsinaHashem/FaithD2T.

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Deductive Closure Training of Language Models for Coherence, Accuracy, and Updatability
Afra Feyza Akyürek | Ekin Akyürek | Leshem Choshen | Derry Wijaya | Jacob Andreas
Findings of the Association for Computational Linguistics ACL 2024

While language models (LMs) can sometimes generate factually correct text and estimate truth values of individual claims, these generally do not reflect a globally coherent, manipulable model of the world. As a consequence, current LMs also generate incorrect or nonsensical content, and are difficult to edit and bring up to date. We present a method called Deductive Closure Training (DCT) that uses LMs themselves to identify implications of (and contradictions within) the text that they generate, yielding an efficient self-supervised procedure for improving LM factuality. Given a collection of seed documents, DCT prompts LMs to generate additional text implied by these documents, reason globally about the correctness of this generated text, and finally fine-tune on text inferred to be correct. Given seed documents from a trusted source, DCT provides a tool for supervised model updating; if seed documents are sampled from the LM itself, DCT enables fully unsupervised fine-tuning for improved coherence and accuracy. Across the CREAK, MQuAKE, and Reversal Curse datasets, supervised DCT improves LM fact verification and text generation accuracy by 3-26%; on CREAK, fully unsupervised DCT improves verification accuracy by 12%. These results show that LMs’ reasoning capabilities during inference can be leveraged during training to improve their reliability.

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Enhancing Emotion Prediction in News Headlines: Insights from ChatGPT and Seq2Seq Models for Free-Text Generation
Ge Gao | Jongin Kim | Sejin Paik | Ekaterina Novozhilova | Yi Liu | Sarah T. Bonna | Margrit Betke | Derry Tanti Wijaya
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Predicting emotions elicited by news headlines can be challenging as the task is largely influenced by the varying nature of people’s interpretations and backgrounds. Previous works have explored classifying discrete emotions directly from news headlines. We provide a different approach to tackling this problem by utilizing people’s explanations of their emotion, written in free-text, on how they feel after reading a news headline. Using the dataset BU-NEmo+ (Gao et al., 2022), we found that for emotion classification, the free-text explanations have a strong correlation with the dominant emotion elicited by the headlines. The free-text explanations also contain more sentimental context than the news headlines alone and can serve as a better input to emotion classification models. Therefore, in this work we explored generating emotion explanations from headlines by training a sequence-to-sequence transformer model and by using pretrained large language model, ChatGPT (GPT-4). We then used the generated emotion explanations for emotion classification. In addition, we also experimented with training the pretrained T5 model for the intermediate task of explanation generation before fine-tuning it for emotion classification. Using McNemar’s significance test, methods that incorporate GPT-generated free-text emotion explanations demonstrated significant improvement (P-value < 0.05) in emotion classification from headlines, compared to methods that only use headlines. This underscores the value of using intermediate free-text explanations for emotion prediction tasks with headlines.

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Mitigating Translationese in Low-resource Languages: The Storyboard Approach
Garry Kuwanto | Eno-Abasi E. Urua | Priscilla Amondi Amuok | Shamsuddeen Hassan Muhammad | Anuoluwapo Aremu | Verrah Otiende | Loice Emma Nanyanga | Teresiah W. Nyoike | Aniefon D. Akpan | Nsima Ab Udouboh | Idongesit Udeme Archibong | Idara Effiong Moses | Ifeoluwatayo A. Ige | Benjamin Ajibade | Olumide Benjamin Awokoya | Idris Abdulmumin | Saminu Mohammad Aliyu | Ruqayya Nasir Iro | Ibrahim Said Ahmad | Deontae Smith | Praise-EL Michaels | David Ifeoluwa Adelani | Derry Tanti Wijaya | Anietie Andy
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Low-resource languages often face challenges in acquiring high-quality language data due to the reliance on translation-based methods, which can introduce the translationese effect. This phenomenon results in translated sentences that lack fluency and naturalness in the target language. In this paper, we propose a novel approach for data collection by leveraging storyboards to elicit more fluent and natural sentences. Our method involves presenting native speakers with visual stimuli in the form of storyboards and collecting their descriptions without direct exposure to the source text. We conducted a comprehensive evaluation comparing our storyboard-based approach with traditional text translation-based methods in terms of accuracy and fluency. Human annotators and quantitative metrics were used to assess translation quality. The results indicate a preference for text translation in terms of accuracy, while our method demonstrates worse accuracy but better fluency in the language focused.

2023

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Explain-then-translate: an analysis on improving program translation with self-generated explanations
Zilu Tang | Mayank Agarwal | Alexander Shypula | Bailin Wang | Derry Wijaya | Jie Chen | Yoon Kim
Findings of the Association for Computational Linguistics: EMNLP 2023

This work explores the use of self-generated natural language explanations as an intermediate step for code-to-code translation with language models. Across three types of explanations and 19 programming languages constructed from the MultiPL-E dataset, we find the explanations to be particularly effective in the zero-shot case, improving performance by 12% on average. Improvements with natural language explanations are particularly pronounced on difficult programs. We release our dataset, code, and canonical solutions in all 19 languages.

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Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings)
Jong C. Park | Yuki Arase | Baotian Hu | Wei Lu | Derry Wijaya | Ayu Purwarianti | Adila Alfa Krisnadhi
Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings)

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Generating Faithful Text From a Knowledge Graph with Noisy Reference Text
Tahsina Hashem | Weiqing Wang | Derry Tanti Wijaya | Mohammed Eunus Ali | Yuan-Fang Li
Proceedings of the 16th International Natural Language Generation Conference

Knowledge Graph (KG)-to-Text generation aims at generating fluent natural-language text that accurately represents the information of a given knowledge graph. While significant progress has been made in this task by exploiting the power of pre-trained language models (PLMs) with appropriate graph structure-aware modules, existing models still fall short of generating faithful text, especially when the ground-truth natural-language text contains additional information that is not present in the graph. In this paper, we develop a KG-to-text generation model that can generate faithful natural-language text from a given graph, in the presence of noisy reference text. Our framework incorporates two core ideas: Firstly, we utilize contrastive learning to enhance the model’s ability to differentiate between faithful and hallucinated information in the text, thereby encouraging the decoder to generate text that aligns with the input graph. Secondly, we empower the decoder to control the level of hallucination in the generated text by employing a controllable text generation technique. We evaluate our model’s performance through the standard quantitative metrics as well as a ChatGPT-based quantitative and qualitative analysis. Our evaluation demonstrates the superior performance of our model over state-of-the-art KG-to-text models on faithfulness.

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Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
Jong C. Park | Yuki Arase | Baotian Hu | Wei Lu | Derry Wijaya | Ayu Purwarianti | Adila Alfa Krisnadhi
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

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Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)
Jong C. Park | Yuki Arase | Baotian Hu | Wei Lu | Derry Wijaya | Ayu Purwarianti | Adila Alfa Krisnadhi
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)

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DUnE: Dataset for Unified Editing
Afra Akyürek | Eric Pan | Garry Kuwanto | Derry Wijaya
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Even the most advanced language models remain susceptible to errors necessitating to modify these models without initiating a comprehensive retraining process. Model editing refers to the modification of a model’s knowledge or representations in a manner that produces the desired outcomes. Prior research primarily centered around editing factual data e.g. “Messi plays for Inter Miami” confining the definition of an edit to a knowledge triplet i.e. (subject, object, relation). However, as the applications of language models expand, so do the diverse ways in which we wish to edit and refine their outputs. In this study, we broaden the scope of the editing problem to include an array of editing cases such as debiasing and rectifying reasoning errors and define an edit as any natural language expression that solicits a change in the model’s outputs. We are introducing DUnE, an editing benchmark where edits are natural language sentences and propose that DUnE presents a challenging yet relevant task. To substantiate this claim, we conduct an extensive series of experiments testing various editing approaches to address DUnE, demonstrating their respective strengths and weaknesses. We argue that retrieval-augmented language modeling can outperform specialized editing techniques and neither set of approaches has fully solved the generalized editing problem covered by our benchmark.

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COVID-19 Vaccine Misinformation in Middle Income Countries
Jongin Kim | Byeo Rhee Bak | Aditya Agrawal | Jiaxi Wu | Veronika Wirtz | Traci Hong | Derry Wijaya
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

This paper introduces a multilingual dataset of COVID-19 vaccine misinformation, consisting of annotated tweets from three middle-income countries: Brazil, Indonesia, and Nigeria. The expertly curated dataset includes annotations for 5,952 tweets, assessing their relevance to COVID-19 vaccines, presence of misinformation, and the themes of the misinformation. To address challenges posed by domain specificity, the low-resource setting, and data imbalance, we adopt two approaches for developing COVID-19 vaccine misinformation detection models: domain-specific pre-training and text augmentation using a large language model. Our best misinformation detection models demonstrate improvements ranging from 2.7 to 15.9 percentage points in macro F1-score compared to the baseline models. Additionally, we apply our misinformation detection models in a large-scale study of 19 million unlabeled tweets from the three countries between 2020 and 2022, showcasing the practical application of our dataset and models for detecting and analyzing vaccine misinformation in multiple countries and languages. Our analysis indicates that percentage changes in the number of new COVID-19 cases are positively associated with COVID-19 vaccine misinformation rates in a staggered manner for Brazil and Indonesia, and there are significant positive associations between the misinformation rates across the three countries.

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RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs
Afra Feyza Akyurek | Ekin Akyurek | Ashwin Kalyan | Peter Clark | Derry Tanti Wijaya | Niket Tandon
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Despite their unprecedented success, even the largest language models make mistakes. Similar to how humans learn and improve using feedback, previous work proposed providing language models with natural language feedback to guide them in repairing their outputs. Because human-generated critiques are expensive to obtain, researchers have devised learned critique generators in lieu of human critics while assuming one can train downstream models to utilize generated feedback. However, this approach does not apply to black-box or limited access models such as ChatGPT, as they cannot be fine-tuned. Moreover, in the era of large general-purpose language agents, fine-tuning is neither computationally nor spatially efficient as it results in multiple copies of the network. In this work, we introduce RL4F (Reinforcement Learning for Feedback), a multi-agent collaborative framework where the critique generator is trained to maximize end-task performance of GPT-3, a fixed model more than 200 times its size. RL4F produces critiques that help GPT-3 revise its outputs. We study three datasets for action planning, summarization and alphabetization and show relative improvements up to 10% in multiple text similarity metrics over other learned, retrieval-augmented or prompting-based critique generators.

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Proceedings of the First Workshop in South East Asian Language Processing
Derry Wijaya | Alham Fikri Aji | Clara Vania | Genta Indra Winata | Ayu Purwarianti
Proceedings of the First Workshop in South East Asian Language Processing

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Replicable Benchmarking of Neural Machine Translation (NMT) on Low-Resource Local Languages in Indonesia
Lucky Susanto | Ryandito Diandaru | Adila Krisnadhi | Ayu Purwarianti | Derry Tanti Wijaya
Proceedings of the First Workshop in South East Asian Language Processing

2022

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Prediction of People’s Emotional Response towards Multi-modal News
Ge Gao | Sejin Paik | Carley Reardon | Yanling Zhao | Lei Guo | Prakash Ishwar | Margrit Betke | Derry Tanti Wijaya
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

We aim to develop methods for understanding how multimedia news exposure can affect people’s emotional responses, and we especially focus on news content related to gun violence, a very important yet polarizing issue in the U.S. We created the dataset NEmo+ by significantly extending the U.S. gun violence news-to-emotions dataset, BU-NEmo, from 320 to 1,297 news headline and lead image pairings and collecting 38,910 annotations in a large crowdsourcing experiment. In curating the NEmo+ dataset, we developed methods to identify news items that will trigger similar versus divergent emotional responses. For news items that trigger similar emotional responses, we compiled them into the NEmo+-Consensus dataset. We benchmark models on this dataset that predict a person’s dominant emotional response toward the target news item (single-label prediction). On the full NEmo+ dataset, containing news items that would lead to both differing and similar emotional responses, we also benchmark models for the novel task of predicting the distribution of evoked emotional responses in humans when presented with multi-modal news content. Our single-label and multi-label prediction models outperform baselines by large margins across several metrics.

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AugCSE: Contrastive Sentence Embedding with Diverse Augmentations
Zilu Tang | Muhammed Yusuf Kocyigit | Derry Tanti Wijaya
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Data augmentation techniques have been proven useful in many applications in NLP fields. Most augmentations are task-specific, and cannot be used as a general-purpose tool. In our work, we present AugCSE, a unified framework to utilize diverse sets of data augmentations to achieve a better, general-purpose, sentence embedding model. Building upon the latest sentence embedding models, our approach uses a simple antagonistic discriminator that differentiates the augmentation types. With the finetuning objective borrowed from domain adaptation, we show that diverse augmentations, which often lead to conflicting contrastive signals, can be tamed to produce a better and more robust sentence representation. Our methods achieve state-of-the-art results on downstream transfer tasks and perform competitively on semantic textual similarity tasks, using only unsupervised data.

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BU-NEmo: an Affective Dataset of Gun Violence News
Carley Reardon | Sejin Paik | Ge Gao | Meet Parekh | Yanling Zhao | Lei Guo | Margrit Betke | Derry Tanti Wijaya
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Given our society’s increased exposure to multimedia formats on social media platforms, efforts to understand how digital content impacts people’s emotions are burgeoning. As such, we introduce a U.S. gun violence news dataset that contains news headline and image pairings from 840 news articles with 15K high-quality, crowdsourced annotations on emotional responses to the news pairings. We created three experimental conditions for the annotation process: two with a single modality (headline or image only), and one multimodal (headline and image together). In contrast to prior works on affectively-annotated data, our dataset includes annotations on the dominant emotion experienced with the content, the intensity of the selected emotion and an open-ended, written component. By collecting annotations on different modalities of the same news content pairings, we explore the relationship between image and text influence on human emotional response. We offer initial analysis on our dataset, showing the nuanced affective differences that appear due to modality and individual factors such as political leaning and media consumption habits. Our dataset is made publicly available to facilitate future research in affective computing.

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Did that happen? Predicting Social Media Posts that are Indicative of what happened in a scene: A case study of a TV show
Anietie Andy | Reno Kriz | Sharath Chandra Guntuku | Derry Tanti Wijaya | Chris Callison-Burch
Proceedings of the Thirteenth Language Resources and Evaluation Conference

While popular Television (TV) shows are airing, some users interested in these shows publish social media posts about the show. Analyzing social media posts related to a TV show can be beneficial for gaining insights about what happened during scenes of the show. This is a challenging task partly because a significant number of social media posts associated with a TV show or event may not clearly describe what happened during the event. In this work, we propose a method to predict social media posts (associated with scenes of a TV show) that are indicative of what transpired during the scenes of the show. We evaluate our method on social media (Twitter) posts associated with an episode of a popular TV show, Game of Thrones. We show that for each of the identified scenes, with high AUC’s, our method can predict posts that are indicative of what happened in a scene from those that are not-indicative. Based on Twitters policy, we will make the Tweeter ID’s of the Twitter posts used for this work publicly available.

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Challenges in Measuring Bias via Open-Ended Language Generation
Afra Feyza Akyürek | Muhammed Yusuf Kocyigit | Sejin Paik | Derry Tanti Wijaya
Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP)

Researchers have devised numerous ways to quantify social biases vested in pretrained language models. As some language models are capable of generating coherent completions given a set of textual prompts, several prompting datasets have been proposed to measure biases between social groups—posing language generation as a way of identifying biases. In this opinion paper, we analyze how specific choices of prompt sets, metrics, automatic tools and sampling strategies affect bias results. We find out that the practice of measuring biases through text completion is prone to yielding contradicting results under different experiment settings. We additionally provide recommendations for reporting biases in open-ended language generation for a more complete outlook of biases exhibited by a given language model. Code to reproduce the results is released under https://github.com/feyzaakyurek/bias-textgen.

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An Unsupervised Approach to Discover Media Frames
Sha Lai | Yanru Jiang | Lei Guo | Margrit Betke | Prakash Ishwar | Derry Tanti Wijaya
Proceedings of the LREC 2022 workshop on Natural Language Processing for Political Sciences

Media framing refers to highlighting certain aspect of an issue in the news to promote a particular interpretation to the audience. Supervised learning has often been used to recognize frames in news articles, requiring a known pool of frames for a particular issue, which must be identified by communication researchers through thorough manual content analysis. In this work, we devise an unsupervised learning approach to discover the frames in news articles automatically. Given a set of news articles for a given issue, e.g., gun violence, our method first extracts frame elements from these articles using related Wikipedia articles and the Wikipedia category system. It then uses a community detection approach to identify frames from these frame elements. We discuss the effectiveness of our approach by comparing the frames it generates in an unsupervised manner to the domain-expert-derived frames for the issue of gun violence, for which a supervised learning model for frame recognition exists.

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Better Quality Estimation for Low Resource Corpus Mining
Muhammed Kocyigit | Jiho Lee | Derry Wijaya
Findings of the Association for Computational Linguistics: ACL 2022

Quality Estimation (QE) models have the potential to change how we evaluate and maybe even train machine translation models. However, these models still lack the robustness to achieve general adoption. We show that Stateof-the-art QE models, when tested in a Parallel Corpus Mining (PCM) setting, perform unexpectedly bad due to a lack of robustness to out-of-domain examples. We propose a combination of multitask training, data augmentation and contrastive learning to achieve better and more robust QE performance. We show that our method improves QE performance significantly in the MLQE challenge and the robustness of QE models when tested in the Parallel Corpus Mining setup. We increase the accuracy in PCM by more than 0.80, making it on par with state-of-the-art PCM methods that use millions of sentence pairs to train their models. In comparison, we use a thousand times less data, 7K parallel sentences in total, and propose a novel low resource PCM method.

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On Measuring Social Biases in Prompt-Based Multi-Task Learning
Afra Feyza Akyürek | Sejin Paik | Muhammed Kocyigit | Seda Akbiyik | Serife Leman Runyun | Derry Wijaya
Findings of the Association for Computational Linguistics: NAACL 2022

Large language models trained on a mixture of NLP tasks that are converted into a text-to-text format using prompts, can generalize into novel forms of language and handle novel tasks. A large body of work within prompt engineering attempts to understand the effects of input forms and prompts in achieving superior performance. We consider an alternative measure and inquire whether the way in which an input is encoded affects social biases promoted in outputs. In this paper, we study T0, a large-scale multi-task text-to-text language model trained using prompt-based learning. We consider two different forms of semantically equivalent inputs: question-answer format and premise-hypothesis format. We use an existing bias benchmark for the former BBQ and create the first bias benchmark in natural language inference BBNLI with hand-written hypotheses while also converting each benchmark into the other form. The results on two benchmarks suggest that given two different formulations of essentially the same input, T0 conspicuously acts more biased in question answering form, which is seen during training, compared to premise-hypothesis form which is unlike its training examples. Code and data are released under https://github.com/feyzaakyurek/bbnli.

2021

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Cultural and Geographical Influences on Image Translatability of Words across Languages
Nikzad Khani | Isidora Tourni | Mohammad Sadegh Rasooli | Chris Callison-Burch | Derry Tanti Wijaya
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Neural Machine Translation (NMT) models have been observed to produce poor translations when there are few/no parallel sentences to train the models. In the absence of parallel data, several approaches have turned to the use of images to learn translations. Since images of words, e.g., horse may be unchanged across languages, translations can be identified via images associated with words in different languages that have a high degree of visual similarity. However, translating via images has been shown to improve upon text-only models only marginally. To better understand when images are useful for translation, we study image translatability of words, which we define as the translatability of words via images, by measuring intra- and inter-cluster similarities of image representations of words that are translations of each other. We find that images of words are not always invariant across languages, and that language pairs with shared culture, meaning having either a common language family, ethnicity or religion, have improved image translatability (i.e., have more similar images for similar words) compared to its converse, regardless of their geographic proximity. In addition, in line with previous works that show images help more in translating concrete words, we found that concrete words have improved image translatability compared to abstract ones.

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Sentiment-based Candidate Selection for NMT
Alexander Jones | Derry Wijaya
Proceedings of Machine Translation Summit XVIII: Research Track

The explosion of user-generated content (UGC)—e.g. social media posts and comments and and reviews—has motivated the development of NLP applications tailored to these types of informal texts. Prevalent among these applications have been sentiment analysis and machine translation (MT). Grounded in the observation that UGC features highly idiomatic and sentiment-charged language and we propose a decoder-side approach that incorporates automatic sentiment scoring into the MT candidate selection process. We train monolingual sentiment classifiers in English and Spanish and in addition to a multilingual sentiment model and by fine-tuning BERT and XLM-RoBERTa. Using n-best candidates generated by a baseline MT model with beam search and we select the candidate that minimizes the absolute difference between the sentiment score of the source sentence and that of the translation and and perform two human evaluations to assess the produced translations. Unlike previous work and we select this minimally divergent translation by considering the sentiment scores of the source sentence and translation on a continuous interval and rather than using e.g. binary classification and allowing for more fine-grained selection of translation candidates. The results of human evaluations show that and in comparison to the open-source MT baseline model on top of which our sentiment-based pipeline is built and our pipeline produces more accurate translations of colloquial and sentiment-heavy source texts.

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Majority Voting with Bidirectional Pre-translation For Bitext Retrieval
Alexander Jones | Derry Tanti Wijaya
Proceedings of the 14th Workshop on Building and Using Comparable Corpora (BUCC 2021)

Obtaining high-quality parallel corpora is of paramount importance for training NMT systems. However, as many language pairs lack adequate gold-standard training data, a popular approach has been to mine so-called “pseudo-parallel” sentences from paired documents in two languages. In this paper, we outline some drawbacks with current methods that rely on an embedding similarity threshold, and propose a heuristic method in its place. Our method involves translating both halves of a paired corpus before mining, and then performing a majority vote on sentence pairs mined in three ways: after translating documents in language x to language y, after translating language y to x, and using the original documents in languages x and y. We demonstrate success with this novel approach on the Tatoeba similarity search benchmark in 64 low-resource languages, and on NMT in Kazakh and Gujarati. We also uncover the effect of resource-related factors (i.e. how much monolingual/bilingual data is available for a given language) on the optimal choice of bitext mining method, demonstrating that there is currently no one-size-fits-all approach for this task. We make the code and data used in our experiments publicly available.

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“Wikily” Supervised Neural Translation Tailored to Cross-Lingual Tasks
Mohammad Sadegh Rasooli | Chris Callison-Burch | Derry Tanti Wijaya
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

We present a simple but effective approach for leveraging Wikipedia for neural machine translation as well as cross-lingual tasks of image captioning and dependency parsing without using any direct supervision from external parallel data or supervised models in the target language. We show that first sentences and titles of linked Wikipedia pages, as well as cross-lingual image captions, are strong signals for a seed parallel data to extract bilingual dictionaries and cross-lingual word embeddings for mining parallel text from Wikipedia. Our final model achieves high BLEU scores that are close to or sometimes higher than strong supervised baselines in low-resource languages; e.g. supervised BLEU of 4.0 versus 12.1 from our model in English-to-Kazakh. Moreover, we tailor our wikily translation models to unsupervised image captioning, and cross-lingual dependency parser transfer. In image captioning, we train a multi-tasking machine translation and image captioning pipeline for Arabic and English from which the Arabic training data is a wikily translation of the English captioning data. Our captioning results on Arabic are slightly better than that of its supervised model. In dependency parsing, we translate a large amount of monolingual text, and use it as an artificial training data in an annotation projection framework. We show that our model outperforms recent work on cross-lingual transfer of dependency parsers.

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OpenFraming: Open-sourced Tool for Computational Framing Analysis of Multilingual Data
Vibhu Bhatia | Vidya Prasad Akavoor | Sejin Paik | Lei Guo | Mona Jalal | Alyssa Smith | David Assefa Tofu | Edward Edberg Halim | Yimeng Sun | Margrit Betke | Prakash Ishwar | Derry Tanti Wijaya
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

When journalists cover a news story, they can cover the story from multiple angles or perspectives. These perspectives are called “frames,” and usage of one frame or another may influence public perception and opinion of the issue at hand. We develop a web-based system for analyzing frames in multilingual text documents. We propose and guide users through a five-step end-to-end computational framing analysis framework grounded in media framing theory in communication research. Users can use the framework to analyze multilingual text data, starting from the exploration of frames in user’s corpora and through review of previous framing literature (step 1-3) to frame classification (step 4) and prediction (step 5). The framework combines unsupervised and supervised machine learning and leverages a state-of-the-art (SoTA) multilingual language model, which can significantly enhance frame prediction performance while requiring a considerably small sample of manual annotations. Through the interactive website, anyone can perform the proposed computational framing analysis, making advanced computational analysis available to researchers without a programming background and bridging the digital divide within the communication research discipline in particular and the academic community in general. The system is available online at http://www.openframing.org, via an API http://www.openframing.org:5000/docs/, or through our GitHub page https://github.com/vibss2397/openFraming.

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IndoCollex: A Testbed for Morphological Transformation of Indonesian Colloquial Words
Haryo Akbarianto Wibowo | Made Nindyatama Nityasya | Afra Feyza Akyürek | Suci Fitriany | Alham Fikri Aji | Radityo Eko Prasojo | Derry Tanti Wijaya
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Detecting Frames in News Headlines and Lead Images in U.S. Gun Violence Coverage
Isidora Tourni | Lei Guo | Taufiq Husada Daryanto | Fabian Zhafransyah | Edward Edberg Halim | Mona Jalal | Boqi Chen | Sha Lai | Hengchang Hu | Margrit Betke | Prakash Ishwar | Derry Tanti Wijaya
Findings of the Association for Computational Linguistics: EMNLP 2021

News media structure their reporting of events or issues using certain perspectives. When describing an incident involving gun violence, for example, some journalists may focus on mental health or gun regulation, while others may emphasize the discussion of gun rights. Such perspectives are called “frames” in communication research. We study, for the first time, the value of combining lead images and their contextual information with text to identify the frame of a given news article. We observe that using multiple modes of information(article- and image-derived features) improves prediction of news frames over any single mode of information when the images are relevant to the frames of the headlines. We also observe that frame image relevance is related to the ease of conveying frames via images, which we call frame concreteness. Additionally, we release the first multimodal news framing dataset related to gun violence in the U.S., curated and annotated by communication researchers. The dataset will allow researchers to further examine the use of multiple information modalities for studying media framing.

2020

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Resolving Pronouns in Twitter Streams: Context can Help!
Anietie Andy | Chris Callison-Burch | Derry Tanti Wijaya
Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference

Many people live-tweet televised events like Presidential debates and popular TV-shows and discuss people or characters in the event. Naturally, many tweets make pronominal reference to these people/characters. We propose an algorithm for resolving personal pronouns that make reference to people involved in an event, in tweet streams collected during the event.

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Informativity in Image Captions vs. Referring Expressions
Elizabeth Coppock | Danielle Dionne | Nathanial Graham | Elias Ganem | Shijie Zhao | Shawn Lin | Wenxing Liu | Derry Wijaya
Proceedings of the Probability and Meaning Conference (PaM 2020)

At the intersection between computer vision and natural language processing, there has been recent progress on two natural language generation tasks: Dense Image Captioning and Referring Expression Generation for objects in complex scenes. The former aims to provide a caption for a specified object in a complex scene for the benefit of an interlocutor who may not be able to see it. The latter aims to produce a referring expression that will serve to identify a given object in a scene that the interlocutor can see. The two tasks are designed for different assumptions about the common ground between the interlocutors, and serve very different purposes, although they both associate a linguistic description with an object in a complex scene. Despite these fundamental differences, the distinction between these two tasks is sometimes overlooked. Here, we undertake a side-by-side comparison between image captioning and reference game human datasets and show that they differ systematically with respect to informativity. We hope that an understanding of the systematic differences among these human datasets will ultimately allow them to be leveraged more effectively in the associated engineering tasks.

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Multi-Label and Multilingual News Framing Analysis
Afra Feyza Akyürek | Lei Guo | Randa Elanwar | Prakash Ishwar | Margrit Betke | Derry Tanti Wijaya
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

News framing refers to the practice in which aspects of specific issues are highlighted in the news to promote a particular interpretation. In NLP, although recent works have studied framing in English news, few have studied how the analysis can be extended to other languages and in a multi-label setting. In this work, we explore multilingual transfer learning to detect multiple frames from just the news headline in a genuinely low-resource context where there are few/no frame annotations in the target language. We propose a novel method that can leverage elementary resources consisting of a dictionary and few annotations to detect frames in the target language. Our method performs comparably or better than translating the entire target language headline to the source language for which we have annotated data. This work opens up an exciting new capability of scaling up frame analysis to many languages, even those without existing translation technologies. Lastly, we apply our method to detect frames on the issue of U.S. gun violence in multiple languages and obtain exciting insights on the relationship between different frames of the same problem across different countries with different languages.

2019

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Detecting Frames in News Headlines and Its Application to Analyzing News Framing Trends Surrounding U.S. Gun Violence
Siyi Liu | Lei Guo | Kate Mays | Margrit Betke | Derry Tanti Wijaya
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Different news articles about the same topic often offer a variety of perspectives: an article written about gun violence might emphasize gun control, while another might promote 2nd Amendment rights, and yet a third might focus on mental health issues. In communication research, these different perspectives are known as “frames”, which, when used in news media will influence the opinion of their readers in multiple ways. In this paper, we present a method for effectively detecting frames in news headlines. Our training and performance evaluation is based on a new dataset of news headlines related to the issue of gun violence in the United States. This Gun Violence Frame Corpus (GVFC) was curated and annotated by journalism and communication experts. Our proposed approach sets a new state-of-the-art performance for multiclass news frame detection, significantly outperforming a recent baseline by 35.9% absolute difference in accuracy. We apply our frame detection approach in a large scale study of 88k news headlines about the coverage of gun violence in the U.S. between 2016 and 2018.

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Winter is here: Summarizing Twitter Streams related to Pre-Scheduled Events
Anietie Andy | Derry Tanti Wijaya | Chris Callison-Burch
Proceedings of the Second Workshop on Storytelling

Pre-scheduled events, such as TV shows and sports games, usually garner considerable attention from the public. Twitter captures large volumes of discussions and messages related to these events, in real-time. Twitter streams related to pre-scheduled events are characterized by the following: (1) spikes in the volume of published tweets reflect the highlights of the event and (2) some of the published tweets make reference to the characters involved in the event, in the context in which they are currently portrayed in a subevent. In this paper, we take advantage of these characteristics to identify the highlights of pre-scheduled events from tweet streams and we demonstrate a method to summarize these highlights. We evaluate our algorithm on tweets collected around 2 episodes of a popular TV show, Game of Thrones, Season 7.

2018

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Learning Translations via Images with a Massively Multilingual Image Dataset
John Hewitt | Daphne Ippolito | Brendan Callahan | Reno Kriz | Derry Tanti Wijaya | Chris Callison-Burch
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We conduct the most comprehensive study to date into translating words via images. To facilitate research on the task, we introduce a large-scale multilingual corpus of images, each labeled with the word it represents. Past datasets have been limited to only a few high-resource languages and unrealistically easy translation settings. In contrast, we have collected by far the largest available dataset for this task, with images for approximately 10,000 words in each of 100 languages. We run experiments on a dozen high resource languages and 20 low resources languages, demonstrating the effect of word concreteness and part-of-speech on translation quality. %We find that while image features work best for concrete nouns, they are sometimes effective on other parts of speech. To improve image-based translation, we introduce a novel method of predicting word concreteness from images, which improves on a previous state-of-the-art unsupervised technique. This allows us to predict when image-based translation may be effective, enabling consistent improvements to a state-of-the-art text-based word translation system. Our code and the Massively Multilingual Image Dataset (MMID) are available at http://multilingual-images.org/.

2017

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Learning Translations via Matrix Completion
Derry Tanti Wijaya | Brendan Callahan | John Hewitt | Jie Gao | Xiao Ling | Marianna Apidianaki | Chris Callison-Burch
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Bilingual Lexicon Induction is the task of learning word translations without bilingual parallel corpora. We model this task as a matrix completion problem, and present an effective and extendable framework for completing the matrix. This method harnesses diverse bilingual and monolingual signals, each of which may be incomplete or noisy. Our model achieves state-of-the-art performance for both high and low resource languages.

2016

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Mapping Verbs in Different Languages to Knowledge Base Relations using Web Text as Interlingua
Derry Tanti Wijaya | Tom M. Mitchell
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2015

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“A Spousal Relation Begins with a Deletion of engage and Ends with an Addition of divorce”: Learning State Changing Verbs from Wikipedia Revision History
Derry Tanti Wijaya | Ndapandula Nakashole | Tom Mitchell
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2014

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CTPs: Contextual Temporal Profiles for Time Scoping Facts using State Change Detection
Derry Tanti Wijaya | Ndapandula Nakashole | Tom M. Mitchell
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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