Florian Metze


2023

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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.

2022

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Phone Inventories and Recognition for Every Language
Xinjian Li | Florian Metze | David R. Mortensen | Alan W Black | Shinji Watanabe
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Identifying phone inventories is a crucial component in language documentation and the preservation of endangered languages. However, even the largest collection of phone inventory only covers about 2000 languages, which is only 1/4 of the total number of languages in the world. A majority of the remaining languages are endangered. In this work, we attempt to solve this problem by estimating the phone inventory for any language listed in Glottolog, which contains phylogenetic information regarding 8000 languages. In particular, we propose one probabilistic model and one non-probabilistic model, both using phylogenetic trees (“language family trees”) to measure the distance between languages. We show that our best model outperforms baseline models by 6.5 F1. Furthermore, we demonstrate that, with the proposed inventories, the phone recognition model can be customized for every language in the set, which improved the PER (phone error rate) in phone recognition by 25%.

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Normalized Contrastive Learning for Text-Video Retrieval
Yookoon Park | Mahmoud Azab | Seungwhan Moon | Bo Xiong | Florian Metze | Gourab Kundu | Kirmani Ahmed
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Cross-modal contrastive learning has led the recent advances in multimodal retrieval with its simplicity and effectiveness. In this work, however, we reveal that cross-modal contrastive learning suffers from incorrect normalization of the sum retrieval probabilities of each text or video instance. Specifically, we show that many test instances are either over- or under-represented during retrieval, significantly hurting the retrieval performance. To address this problem, we propose Normalized Contrastive Learning (NCL) which utilizes the Sinkhorn-Knopp algorithm to compute the instance-wise biases that properly normalize the sum retrieval probabilities of each instance so that every text and video instance is fairly represented during cross-modal retrieval. Empirical study shows that NCL brings consistent and significant gains in text-video retrieval on different model architectures, with new state-of-the-art multimodal retrieval metrics on the ActivityNet, MSVD, and MSR-VTT datasets without any architecture engineering.

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Zero-shot Learning for Grapheme to Phoneme Conversion with Language Ensemble
Xinjian Li | Florian Metze | David Mortensen | Shinji Watanabe | Alan Black
Findings of the Association for Computational Linguistics: ACL 2022

Grapheme-to-Phoneme (G2P) has many applications in NLP and speech fields. Most existing work focuses heavily on languages with abundant training datasets, which limits the scope of target languages to less than 100 languages. This work attempts to apply zero-shot learning to approximate G2P models for all low-resource and endangered languages in Glottolog (about 8k languages). For any unseen target language, we first build the phylogenetic tree (i.e. language family tree) to identify top-k nearest languages for which we have training sets. Then we run models of those languages to obtain a hypothesis set, which we combine into a confusion network to propose a most likely hypothesis as an approximation to the target language. We test our approach on over 600 unseen languages and demonstrate it significantly outperforms baselines.

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On Advances in Text Generation from Images Beyond Captioning: A Case Study in Self-Rationalization
Shruti Palaskar | Akshita Bhagia | Yonatan Bisk | Florian Metze | Alan W Black | Ana Marasovic
Findings of the Association for Computational Linguistics: EMNLP 2022

Combining the visual modality with pretrained language models has been surprisingly effective for simple descriptive tasks such as image captioning. More general text generation however remains elusive. We take a step back and ask: How do these models work for more complex generative tasks, i.e. conditioning on both text and images? Are multimodal models simply visually adapted language models, or do they combine they reason jointly over modalities?We investigate these questions in the context of self-rationalization (jointly generating task labels/answers and free-text explanations) of three tasks: (i) visual question answering in VQA-X, (ii) visual commonsense reasoning in VCR, and (iii) visual-textual entailment in E-SNLI-VE. We show that recent unimodal advances, CLIP image representations and scaling of language models, do not consistently improveself-rationalization in multimodal tasks. We find that no single model type works universally best across tasks, datasets, and finetuning data sizes. Our findings motivate the need for novel general backbones that move text generation from images and text beyond image captioning.

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Token-level Sequence Labeling for Spoken Language Understanding using Compositional End-to-End Models
Siddhant Arora | Siddharth Dalmia | Brian Yan | Florian Metze | Alan W Black | Shinji Watanabe
Findings of the Association for Computational Linguistics: EMNLP 2022

End-to-end spoken language understanding (SLU) systems are gaining popularity over cascaded approaches due to their simplicity and ability to avoid error propagation. However, these systems model sequence labeling as a sequence prediction task causing a divergence from its well-established token-level tagging formulation. We build compositional end-to-end SLU systems that explicitly separate the added complexity of recognizing spoken mentions in SLU from the NLU task of sequence labeling. By relying on intermediate decoders trained for ASR, our end-to-end systems transform the input modality from speech to token-level representations that can be used in the traditional sequence labeling framework. This composition of ASR and NLU formulations in our end-to-end SLU system offers direct compatibility with pre-trained ASR and NLU systems, allows performance monitoring of individual components and enables the use of globally normalized losses like CRF, making them attractive in practical scenarios. Our models outperform both cascaded and direct end-to-end models on a labeling task of named entity recognition across SLU benchmarks.

2021

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Searchable Hidden Intermediates for End-to-End Models of Decomposable Sequence Tasks
Siddharth Dalmia | Brian Yan | Vikas Raunak | Florian Metze | Shinji Watanabe
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

End-to-end approaches for sequence tasks are becoming increasingly popular. Yet for complex sequence tasks, like speech translation, systems that cascade several models trained on sub-tasks have shown to be superior, suggesting that the compositionality of cascaded systems simplifies learning and enables sophisticated search capabilities. In this work, we present an end-to-end framework that exploits compositionality to learn searchable hidden representations at intermediate stages of a sequence model using decomposed sub-tasks. These hidden intermediates can be improved using beam search to enhance the overall performance and can also incorporate external models at intermediate stages of the network to re-score or adapt towards out-of-domain data. One instance of the proposed framework is a Multi-Decoder model for speech translation that extracts the searchable hidden intermediates from a speech recognition sub-task. The model demonstrates the aforementioned benefits and outperforms the previous state-of-the-art by around +6 and +3 BLEU on the two test sets of Fisher-CallHome and by around +3 and +4 BLEU on the English-German and English-French test sets of MuST-C.

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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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NoiseQA: Challenge Set Evaluation for User-Centric Question Answering
Abhilasha Ravichander | Siddharth Dalmia | Maria Ryskina | Florian Metze | Eduard Hovy | Alan W Black
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

When Question-Answering (QA) systems are deployed in the real world, users query them through a variety of interfaces, such as speaking to voice assistants, typing questions into a search engine, or even translating questions to languages supported by the QA system. While there has been significant community attention devoted to identifying correct answers in passages assuming a perfectly formed question, we show that components in the pipeline that precede an answering engine can introduce varied and considerable sources of error, and performance can degrade substantially based on these upstream noise sources even for powerful pre-trained QA models. We conclude that there is substantial room for progress before QA systems can be effectively deployed, highlight the need for QA evaluation to expand to consider real-world use, and hope that our findings will spur greater community interest in the issues that arise when our systems actually need to be of utility to humans.

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VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding
Hu Xu | Gargi Ghosh | Po-Yao Huang | Dmytro Okhonko | Armen Aghajanyan | Florian Metze | Luke Zettlemoyer | Christoph Feichtenhofer
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

We present VideoCLIP, a contrastive approach to pre-train a unified model for zero-shot video and text understanding, without using any labels on downstream tasks. VideoCLIP trains a transformer for video and text by contrasting temporally overlapping positive video-text pairs with hard negatives from nearest neighbor retrieval. Our experiments on a diverse series of downstream tasks, including sequence-level text-video retrieval, VideoQA, token-level action localization, and action segmentation reveal state-of-the-art performance, surpassing prior work, and in some cases even outperforming supervised approaches. Code is made available at https://github.com/pytorch/fairseq/examples/MMPT.

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VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding
Hu Xu | Gargi Ghosh | Po-Yao Huang | Prahal Arora | Masoumeh Aminzadeh | Christoph Feichtenhofer | Florian Metze | Luke Zettlemoyer
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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Fine-Grained Grounding for Multimodal Speech Recognition
Tejas Srinivasan | Ramon Sanabria | Florian Metze | Desmond Elliott
Findings of the Association for Computational Linguistics: EMNLP 2020

Multimodal automatic speech recognition systems integrate information from images to improve speech recognition quality, by grounding the speech in the visual context. While visual signals have been shown to be useful for recovering entities that have been masked in the audio, these models should be capable of recovering a broader range of word types. Existing systems rely on global visual features that represent the entire image, but localizing the relevant regions of the image will make it possible to recover a larger set of words, such as adjectives and verbs. In this paper, we propose a model that uses finer-grained visual information from different parts of the image, using automatic object proposals. In experiments on the Flickr8K Audio Captions Corpus, we find that our model improves over approaches that use global visual features, that the proposals enable the model to recover entities and other related words, such as adjectives, and that improvements are due to the model’s ability to localize the correct proposals.

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On Long-Tailed Phenomena in Neural Machine Translation
Vikas Raunak | Siddharth Dalmia | Vivek Gupta | Florian Metze
Findings of the Association for Computational Linguistics: EMNLP 2020

State-of-the-art Neural Machine Translation (NMT) models struggle with generating low-frequency tokens, tackling which remains a major challenge. The analysis of long-tailed phenomena in the context of structured prediction tasks is further hindered by the added complexities of search during inference. In this work, we quantitatively characterize such long-tailed phenomena at two levels of abstraction, namely, token classification and sequence generation. We propose a new loss function, the Anti-Focal loss, to better adapt model training to the structural dependencies of conditional text generation by incorporating the inductive biases of beam search in the training process. We show the efficacy of the proposed technique on a number of Machine Translation (MT) datasets, demonstrating that it leads to significant gains over cross-entropy across different language pairs, especially on the generation of low-frequency words. We have released the code to reproduce our results.

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Multimodal Speech Recognition with Unstructured Audio Masking
Tejas Srinivasan | Ramon Sanabria | Florian Metze | Desmond Elliott
Proceedings of the First International Workshop on Natural Language Processing Beyond Text

Visual context has been shown to be useful for automatic speech recognition (ASR) systems when the speech signal is noisy or corrupted. Previous work, however, has only demonstrated the utility of visual context in an unrealistic setting, where a fixed set of words are systematically masked in the audio. In this paper, we simulate a more realistic masking scenario during model training, called RandWordMask, where the masking can occur for any word segment. Our experiments on the Flickr 8K Audio Captions Corpus show that multimodal ASR can generalize to recover different types of masked words in this unstructured masking setting. Moreover, our analysis shows that our models are capable of attending to the visual signal when the audio signal is corrupted. These results show that multimodal ASR systems can leverage the visual signal in more generalized noisy scenarios.

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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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On Dimensional Linguistic Properties of the Word Embedding Space
Vikas Raunak | Vaibhav Kumar | Vivek Gupta | Florian Metze
Proceedings of the 5th Workshop on Representation Learning for NLP

Word embeddings have become a staple of several natural language processing tasks, yet much remains to be understood about their properties. In this work, we analyze word embeddings in terms of their principal components and arrive at a number of novel and counterintuitive observations. In particular, we characterize the utility of variance explained by the principal components as a proxy for downstream performance. Furthermore, through syntactic probing of the principal embedding space, we show that the syntactic information captured by a principal component does not correlate with the amount of variance it explains. Consequently, we investigate the limitations of variance based embedding post-processing algorithms and demonstrate that such post-processing is counter-productive in sentence classification and machine translation tasks. Finally, we offer a few precautionary guidelines on applying variance based embedding post-processing and explain why non-isotropic geometry might be integral to word embedding performance.

2019

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Gated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion
Suyoun Kim | Siddharth Dalmia | Florian Metze
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We present a novel conversational-context aware end-to-end speech recognizer based on a gated neural network that incorporates conversational-context/word/speech embeddings. Unlike conventional speech recognition models, our model learns longer conversational-context information that spans across sentences and is consequently better at recognizing long conversations. Specifically, we propose to use text-based external word and/or sentence embeddings (i.e., fastText, BERT) within an end-to-end framework, yielding significant improvement in word error rate with better conversational-context representation. We evaluated the models on the Switchboard conversational speech corpus and show that our model outperforms standard end-to-end speech recognition models.

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Multimodal Abstractive Summarization for How2 Videos
Shruti Palaskar | Jindřich Libovický | Spandana Gella | Florian Metze
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

In this paper, we study abstractive summarization for open-domain videos. Unlike the traditional text news summarization, the goal is less to “compress” text information but rather to provide a fluent textual summary of information that has been collected and fused from different source modalities, in our case video and audio transcripts (or text). We show how a multi-source sequence-to-sequence model with hierarchical attention can integrate information from different modalities into a coherent output, compare various models trained with different modalities and present pilot experiments on the How2 corpus of instructional videos. We also propose a new evaluation metric (Content F1) for abstractive summarization task that measures semantic adequacy rather than fluency of the summaries, which is covered by metrics like ROUGE and BLEU.

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Acoustic-to-Word Models with Conversational Context Information
Suyoun Kim | Florian Metze
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)

Conversational context information, higher-level knowledge that spans across sentences, can help to recognize a long conversation. However, existing speech recognition models are typically built at a sentence level, and thus it may not capture important conversational context information. The recent progress in end-to-end speech recognition enables integrating context with other available information (e.g., acoustic, linguistic resources) and directly recognizing words from speech. In this work, we present a direct acoustic-to-word, end-to-end speech recognition model capable of utilizing the conversational context to better process long conversations. We evaluate our proposed approach on the Switchboard conversational speech corpus and show that our system outperforms a standard end-to-end speech recognition system.

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CMU’s Machine Translation System for IWSLT 2019
Tejas Srinivasan | Ramon Sanabria | Florian Metze
Proceedings of the 16th International Conference on Spoken Language Translation

In Neural Machine Translation (NMT) the usage of sub-words and characters as source and target units offers a simple and flexible solution for translation of rare and unseen words. However, selecting the optimal subword segmentation involves a trade-off between expressiveness and flexibility, and is language and dataset-dependent. We present Block Multitask Learning (BMTL), a novel NMT architecture that predicts multiple targets of different granularities simulta- neously, removing the need to search for the optimal seg- mentation strategy. Our multi-task model exhibits improvements of up to 1.7 BLEU points on each decoder over single-task baseline models with the same number of parameters on datasets from two language pairs of IWSLT15 and one from IWSLT19. The multiple hypotheses generated at different granularities can also be combined as a post-processing step to give better translations.

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Multitask Learning For Different Subword Segmentations In Neural Machine Translation
Tejas Srinivasan | Ramon Sanabria | Florian Metze
Proceedings of the 16th International Conference on Spoken Language Translation

In Neural Machine Translation (NMT) the usage of sub􏰃words and characters as source and target units offers a simple and flexible solution for translation of rare and unseen words. However, selecting the optimal subword segmentation involves a trade-off between expressiveness and flexibility, and is language and dataset-dependent. We present Block Multitask Learning (BMTL), a novel NMT architecture that predicts multiple targets of different granularities simultaneously, removing the need to search for the optimal segmentation strategy. Our multi-task model exhibits improvements of up to 1.7 BLEU points on each decoder over single-task baseline models with the same number of parameters on datasets from two language pairs of IWSLT15 and one from IWSLT19. The multiple hypotheses generated at different granularities can be combined as a post-processing step to give better translations, which improves over hypothesis combination from baseline models while using substantially fewer parameters.

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Effective Dimensionality Reduction for Word Embeddings
Vikas Raunak | Vivek Gupta | Florian Metze
Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)

Pre-trained word embeddings are used in several downstream applications as well as for constructing representations for sentences, paragraphs and documents. Recently, there has been an emphasis on improving the pretrained word vectors through post-processing algorithms. One improvement area is reducing the dimensionality of word embeddings. Reducing the size of word embeddings can improve their utility in memory constrained devices, benefiting several real world applications. In this work, we present a novel technique that efficiently combines PCA based dimensionality reduction with a recently proposed post-processing algorithm (Mu and Viswanath, 2018), to construct effective word embeddings of lower dimensions. Empirical evaluations on several benchmarks show that our algorithm efficiently reduces the embedding size while achieving similar or (more often) better performance than original embeddings. We have released the source code along with this paper.

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On Leveraging the Visual Modality for Neural Machine Translation
Vikas Raunak | Sang Keun Choe | Quanyang Lu | Yi Xu | Florian Metze
Proceedings of the 12th International Conference on Natural Language Generation

Leveraging the visual modality effectively for Neural Machine Translation (NMT) remains an open problem in computational linguistics. Recently, Caglayan et al. posit that the observed gains are limited mainly due to the very simple, short, repetitive sentences of the Multi30k dataset (the only multimodal MT dataset available at the time), which renders the source text sufficient for context. In this work, we further investigate this hypothesis on a new large scale multimodal Machine Translation (MMT) dataset, How2, which has 1.57 times longer mean sentence length than Multi30k and no repetition. We propose and evaluate three novel fusion techniques, each of which is designed to ensure the utilization of visual context at different stages of the Sequence-to-Sequence transduction pipeline, even under full linguistic context. However, we still obtain only marginal gains under full linguistic context and posit that visual embeddings extracted from deep vision models (ResNet for Multi30k, ResNext for How2) do not lend themselves to increasing the discriminativeness between the vocabulary elements at token level prediction in NMT. We demonstrate this qualitatively by analyzing attention distribution and quantitatively through Principal Component Analysis, arriving at the conclusion that it is the quality of the visual embeddings rather than the length of sentences, which need to be improved in existing MMT datasets.

2018

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Annotating High-Level Structures of Short Stories and Personal Anecdotes
Boyang Li | Beth Cardier | Tong Wang | Florian Metze
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2014

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Semantics for Large-Scale Multimedia: New Challenges for NLP
Florian Metze | Koichi Shinoda
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: Tutorials

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Augmenting Translation Models with Simulated Acoustic Confusions for Improved Spoken Language Translation
Yulia Tsvetkov | Florian Metze | Chris Dyer
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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Multilingual deep bottle neck features: a study on language selection and training techniques
Markus Müller | Sebastian Stüker | Zaid Sheikh | Florian Metze | Alex Waibel
Proceedings of the 11th International Workshop on Spoken Language Translation: Papers

Previous work has shown that training the neural networks for bottle neck feature extraction in a multilingual way can lead to improvements in word error rate and average term weighted value in a telephone key word search task. In this work we conduct a systematic study on a) which multilingual training strategy to employ, b) the effect of language selection and amount of multilingual training data used and c) how to find a suitable combination for languages. We conducted our experiment on the key word search task and the languages of the IARPA BABEL program. In a first step, we assessed the performance of a single language out of all available languages in combination with the target language. Based on these results, we then combined a multitude of languages. We also examined the influence of the amount of training data per language, as well as different techniques for combining the languages during network training. Our experiments show that data from arbitrary additional languages does not necessarily increase the performance of a system. But when combining a suitable set of languages, a significant gain in performance can be achieved.

2013

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Prosody-Based Unsupervised Speech Summarization with Two-Layer Mutually Reinforced Random Walk
Sujay Kumar Jauhar | Yun-Nung Chen | Florian Metze
Proceedings of the Sixth International Joint Conference on Natural Language Processing

2012

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Generating Natural Language Summaries for Multimedia
Duo Ding | Florian Metze | Shourabh Rawat | Peter Schulam | Susanne Burger
INLG 2012 Proceedings of the Seventh International Natural Language Generation Conference

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Intra-Speaker Topic Modeling for Improved Multi-Party Meeting Summarization with Integrated Random Walk
Yun-Nung Chen | Florian Metze
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2007

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On using Articulatory Features for Discriminative Speaker Adaptation
Florian Metze
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers

2002

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A Multi-Perspective Evaluation of the NESPOLE! Speech-to-Speech Translation System
Alon Lavie | Florian Metze | Roldano Cattoni | Erica Costantini
Proceedings of the ACL-02 Workshop on Speech-to-Speech Translation: Algorithms and Systems

2001

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Advances in meeting recognition
Alex Waibel | Hua Yu | Tanja Schultz | Yue Pan | Michael Bett | Martin Westphal | Hagen Soltau | Thomas Schaaf | Florian Metze
Proceedings of the First International Conference on Human Language Technology Research