Myle Ott


2022

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Few-shot Learning with Multilingual Generative Language Models
Xi Victoria Lin | Todor Mihaylov | Mikel Artetxe | Tianlu Wang | Shuohui Chen | Daniel Simig | Myle Ott | Naman Goyal | Shruti Bhosale | Jingfei Du | Ramakanth Pasunuru | Sam Shleifer | Punit Singh Koura | Vishrav Chaudhary | Brian O’Horo | Jeff Wang | Luke Zettlemoyer | Zornitsa Kozareva | Mona Diab | Veselin Stoyanov | Xian Li
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Large-scale generative language models such as GPT-3 are competitive few-shot learners. While these models are known to be able to jointly represent many different languages, their training data is dominated by English, potentially limiting their cross-lingual generalization. In this work, we train multilingual generative language models on a corpus covering a diverse set of languages, and study their few- and zero-shot learning capabilities in a wide range of tasks. Our largest model with 7.5 billion parameters sets new state of the art in few-shot learning in more than 20 representative languages, outperforming GPT-3 of comparable size in multilingual commonsense reasoning (with +7.4% absolute accuracy improvement in 0-shot settings and +9.4% in 4-shot settings) and natural language inference (+5.4% in each of 0-shot and 4-shot settings). On the FLORES-101 machine translation benchmark, our model outperforms GPT-3 on 171 out of 182 directions with 32 training examples, while surpassing the official supervised baseline in 45 directions. We conduct an in-depth analysis of different multilingual prompting approaches, showing in particular that strong few-shot learning performance across languages can be achieved via cross-lingual transfer through both templates and demonstration examples.

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Efficient Large Scale Language Modeling with Mixtures of Experts
Mikel Artetxe | Shruti Bhosale | Naman Goyal | Todor Mihaylov | Myle Ott | Sam Shleifer | Xi Victoria Lin | Jingfei Du | Srinivasan Iyer | Ramakanth Pasunuru | Giridharan Anantharaman | Xian Li | Shuohui Chen | Halil Akin | Mandeep Baines | Louis Martin | Xing Zhou | Punit Singh Koura | Brian O’Horo | Jeffrey Wang | Luke Zettlemoyer | Mona Diab | Zornitsa Kozareva | Veselin Stoyanov
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Mixture of Experts layers (MoEs) enable efficient scaling of language models through conditional computation. This paper presents a detailed empirical study of how autoregressive MoE language models scale in comparison with dense models in a wide range of settings: in- and out-of-domain language modeling, zero- and few-shot priming, and full-shot fine-tuning. With the exception of fine-tuning, we find MoEs to be substantially more compute efficient. At more modest training budgets, MoEs can match the performance of dense models using ~4 times less compute. This gap narrows at scale, but our largest MoE model (1.1T parameters) consistently outperforms a compute-equivalent dense model (6.7B parameters). Overall, this performance gap varies greatly across tasks and domains, suggesting that MoE and dense models generalize differently in ways that are worthy of future study. We make our code and models publicly available for research use.

2021

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Recipes for Building an Open-Domain Chatbot
Stephen Roller | Emily Dinan | Naman Goyal | Da Ju | Mary Williamson | Yinhan Liu | Jing Xu | Myle Ott | Eric Michael Smith | Y-Lan Boureau | Jason Weston
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we highlight other ingredients. Good conversation requires blended skills: providing engaging talking points, and displaying knowledge, empathy and personality appropriately, while maintaining a consistent persona. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter models, and make our models and code publicly available. Human evaluations show our best models outperform existing approaches in multi-turn dialogue on engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.

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Analyzing the Forgetting Problem in Pretrain-Finetuning of Open-domain Dialogue Response Models
Tianxing He | Jun Liu | Kyunghyun Cho | Myle Ott | Bing Liu | James Glass | Fuchun Peng
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

In this work, we study how the finetuning stage in the pretrain-finetune framework changes the behavior of a pretrained neural language generator. We focus on the transformer encoder-decoder model for the open-domain dialogue response generation task. Our major finding is that after standard finetuning, the model forgets some of the important language generation skills acquired during large-scale pretraining. We demonstrate the forgetting phenomenon through a set of detailed behavior analysis from the perspectives of knowledge transfer, context sensitivity, and function space projection. As a preliminary attempt to alleviate the forgetting problem, we propose an intuitive finetuning strategy named “mix-review”. We find that mix-review effectively regularizes the finetuning process, and the forgetting problem is alleviated to some extent. Finally, we discuss interesting behavior of the resulting dialogue model and its implications.

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The Source-Target Domain Mismatch Problem in Machine Translation
Jiajun Shen | Peng-Jen Chen | Matthew Le | Junxian He | Jiatao Gu | Myle Ott | Michael Auli | Marc’Aurelio Ranzato
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

While we live in an increasingly interconnected world, different places still exhibit strikingly different cultures and many events we experience in our every day life pertain only to the specific place we live in. As a result, people often talk about different things in different parts of the world. In this work we study the effect of local context in machine translation and postulate that this causes the domains of the source and target language to greatly mismatch. We first formalize the concept of source-target domain mismatch, propose a metric to quantify it, and provide empirical evidence for its existence. We conclude with an empirical study of how source-target domain mismatch affects training of machine translation systems on low resource languages. While this may severely affect back-translation, the degradation can be alleviated by combining back-translation with self-training and by increasing the amount of target side monolingual data.

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Larger-Scale Transformers for Multilingual Masked Language Modeling
Naman Goyal | Jingfei Du | Myle Ott | Giri Anantharaman | Alexis Conneau
Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)

Recent work has demonstrated the effectiveness of cross-lingual language model pretraining for cross-lingual understanding. In this study, we present the results of two larger multilingual masked language models, with 3.5B and 10.7B parameters. Our two new models dubbed and outperform XLM-R by 1.8% and 2.4% average accuracy on XNLI. Our model also outperforms the RoBERTa-Large model on several English tasks of the GLUE benchmark by 0.3% on average while handling 99 more languages. This suggests larger capacity models for language understanding may obtain strong performance on high-resource languages while greatly improving low-resource languages. We make our code and models publicly available.

2020

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Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art
Patrick Lewis | Myle Ott | Jingfei Du | Veselin Stoyanov
Proceedings of the 3rd Clinical Natural Language Processing Workshop

A large array of pretrained models are available to the biomedical NLP (BioNLP) community. Finding the best model for a particular task can be difficult and time-consuming. For many applications in the biomedical and clinical domains, it is crucial that models can be built quickly and are highly accurate. We present a large-scale study across 18 established biomedical and clinical NLP tasks to determine which of several popular open-source biomedical and clinical NLP models work well in different settings. Furthermore, we apply recent advances in pretraining to train new biomedical language models, and carefully investigate the effect of various design choices on downstream performance. Our best models perform well in all of our benchmarks, and set new State-of-the-Art in 9 tasks. We release these models in the hope that they can help the community to speed up and increase the accuracy of BioNLP and text mining applications.

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How Decoding Strategies Affect the Verifiability of Generated Text
Luca Massarelli | Fabio Petroni | Aleksandra Piktus | Myle Ott | Tim Rocktäschel | Vassilis Plachouras | Fabrizio Silvestri | Sebastian Riedel
Findings of the Association for Computational Linguistics: EMNLP 2020

Recent progress in pre-trained language models led to systems that are able to generate text of an increasingly high quality. While several works have investigated the fluency and grammatical correctness of such models, it is still unclear to which extent the generated text is consistent with factual world knowledge. Here, we go beyond fluency and also investigate the verifiability of text generated by state-of-the-art pre-trained language models. A generated sentence is verifiable if it can be corroborated or disproved by Wikipedia, and we find that the verifiability of generated text strongly depends on the decoding strategy. In particular, we discover a tradeoff between factuality (i.e., the ability of generating Wikipedia corroborated text) and repetitiveness. While decoding strategies such as top-k and nucleus sampling lead to less repetitive generations, they also produce less verifiable text. Based on these finding, we introduce a simple and effective decoding strategy which, in comparison to previously used decoding strategies, produces less repetitive and more verifiable text.

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General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference
Jingfei Du | Myle Ott | Haoran Li | Xing Zhou | Veselin Stoyanov
Findings of the Association for Computational Linguistics: EMNLP 2020

The state of the art on many NLP tasks is currently achieved by large pre-trained language models, which require a considerable amount of computation. We aim to reduce the inference cost in a setting where many different predictions are made on a single piece of text. In that case, computational cost during inference can be amortized over the different predictions (tasks) using a shared text encoder. We compare approaches for training such an encoder and show that encoders pre-trained over multiple tasks generalize well to unseen tasks. We also compare ways of extracting fixed- and limited-size representations from this encoder, including pooling features extracted from multiple layers or positions. Our best approach compares favorably to knowledge distillation, achieving higher accuracy and lower computational cost once the system is handling around 7 tasks. Further, we show that through binary quantization, we can reduce the size of the extracted representations by a factor of 16 to store them for later use. The resulting method offers a compelling solution for using large-scale pre-trained models at a fraction of the computational cost when multiple tasks are performed on the same text.

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On The Evaluation of Machine Translation Systems Trained With Back-Translation
Sergey Edunov | Myle Ott | Marc’Aurelio Ranzato | Michael Auli
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Back-translation is a widely used data augmentation technique which leverages target monolingual data. However, its effectiveness has been challenged since automatic metrics such as BLEU only show significant improvements for test examples where the source itself is a translation, or translationese. This is believed to be due to translationese inputs better matching the back-translated training data. In this work, we show that this conjecture is not empirically supported and that back-translation improves translation quality of both naturally occurring text as well as translationese according to professional human translators. We provide empirical evidence to support the view that back-translation is preferred by humans because it produces more fluent outputs. BLEU cannot capture human preferences because references are translationese when source sentences are natural text. We recommend complementing BLEU with a language model score to measure fluency.

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Unsupervised Cross-lingual Representation Learning at Scale
Alexis Conneau | Kartikay Khandelwal | Naman Goyal | Vishrav Chaudhary | Guillaume Wenzek | Francisco Guzmán | Edouard Grave | Myle Ott | Luke Zettlemoyer | Veselin Stoyanov
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +14.6% average accuracy on XNLI, +13% average F1 score on MLQA, and +2.4% F1 score on NER. XLM-R performs particularly well on low-resource languages, improving 15.7% in XNLI accuracy for Swahili and 11.4% for Urdu over previous XLM models. We also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-R is very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make our code and models publicly available.

2019

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fairseq: A Fast, Extensible Toolkit for Sequence Modeling
Myle Ott | Sergey Edunov | Alexei Baevski | Angela Fan | Sam Gross | Nathan Ng | David Grangier | Michael Auli
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)

fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks. The toolkit is based on PyTorch and supports distributed training across multiple GPUs and machines. We also support fast mixed-precision training and inference on modern GPUs. A demo video can be found at https://www.youtube.com/watch?v=OtgDdWtHvto

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The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali–English and Sinhala–English
Francisco Guzmán | Peng-Jen Chen | Myle Ott | Juan Pino | Guillaume Lample | Philipp Koehn | Vishrav Chaudhary | Marc’Aurelio Ranzato
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

For machine translation, a vast majority of language pairs in the world are considered low-resource because they have little parallel data available. Besides the technical challenges of learning with limited supervision, it is difficult to evaluate methods trained on low-resource language pairs because of the lack of freely and publicly available benchmarks. In this work, we introduce the FLORES evaluation datasets for Nepali–English and Sinhala– English, based on sentences translated from Wikipedia. Compared to English, these are languages with very different morphology and syntax, for which little out-of-domain parallel data is available and for which relatively large amounts of monolingual data are freely available. We describe our process to collect and cross-check the quality of translations, and we report baseline performance using several learning settings: fully supervised, weakly supervised, semi-supervised, and fully unsupervised. Our experiments demonstrate that current state-of-the-art methods perform rather poorly on this benchmark, posing a challenge to the research community working on low-resource MT. Data and code to reproduce our experiments are available at https://github.com/facebookresearch/flores.

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Facebook AI’s WAT19 Myanmar-English Translation Task Submission
Peng-Jen Chen | Jiajun Shen | Matthew Le | Vishrav Chaudhary | Ahmed El-Kishky | Guillaume Wenzek | Myle Ott | Marc’Aurelio Ranzato
Proceedings of the 6th Workshop on Asian Translation

This paper describes Facebook AI’s submission to the WAT 2019 Myanmar-English translation task. Our baseline systems are BPE-based transformer models. We explore methods to leverage monolingual data to improve generalization, including self-training, back-translation and their combination. We further improve results by using noisy channel re-ranking and ensembling. We demonstrate that these techniques can significantly improve not only a system trained with additional monolingual data, but even the baseline system trained exclusively on the provided small parallel dataset. Our system ranks first in both directions according to human evaluation and BLEU, with a gain of over 8 BLEU points above the second best system.

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Facebook FAIR’s WMT19 News Translation Task Submission
Nathan Ng | Kyra Yee | Alexei Baevski | Myle Ott | Michael Auli | Sergey Edunov
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

This paper describes Facebook FAIR’s submission to the WMT19 shared news translation task. We participate in four language directions, English <-> German and English <-> Russian in both directions. Following our submission from last year, our baseline systems are large BPE-based transformer models trained with the FAIRSEQ sequence modeling toolkit. This year we experiment with different bitext data filtering schemes, as well as with adding filtered back-translated data. We also ensemble and fine-tune our models on domain-specific data, then decode using noisy channel model reranking. Our system improves on our previous system’s performance by 4.5 BLEU points and achieves the best case-sensitive BLEU score for the translation direction English→Russian.

2018

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Scaling Neural Machine Translation
Myle Ott | Sergey Edunov | David Grangier | Michael Auli
Proceedings of the Third Conference on Machine Translation: Research Papers

Sequence to sequence learning models still require several days to reach state of the art performance on large benchmark datasets using a single machine. This paper shows that reduced precision and large batch training can speedup training by nearly 5x on a single 8-GPU machine with careful tuning and implementation. On WMT’14 English-German translation, we match the accuracy of Vaswani et al. (2017) in under 5 hours when training on 8 GPUs and we obtain a new state of the art of 29.3 BLEU after training for 85 minutes on 128 GPUs. We further improve these results to 29.8 BLEU by training on the much larger Paracrawl dataset. On the WMT’14 English-French task, we obtain a state-of-the-art BLEU of 43.2 in 8.5 hours on 128 GPUs.

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Understanding Back-Translation at Scale
Sergey Edunov | Myle Ott | Michael Auli | David Grangier
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

An effective method to improve neural machine translation with monolingual data is to augment the parallel training corpus with back-translations of target language sentences. This work broadens the understanding of back-translation and investigates a number of methods to generate synthetic source sentences. We find that in all but resource poor settings back-translations obtained via sampling or noised beam outputs are most effective. Our analysis shows that sampling or noisy synthetic data gives a much stronger training signal than data generated by beam or greedy search. We also compare how synthetic data compares to genuine bitext and study various domain effects. Finally, we scale to hundreds of millions of monolingual sentences and achieve a new state of the art of 35 BLEU on the WMT’14 English-German test set.

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Phrase-Based & Neural Unsupervised Machine Translation
Guillaume Lample | Myle Ott | Alexis Conneau | Ludovic Denoyer | Marc’Aurelio Ranzato
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Machine translation systems achieve near human-level performance on some languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences, which hinders their applicability to the majority of language pairs. This work investigates how to learn to translate when having access to only large monolingual corpora in each language. We propose two model variants, a neural and a phrase-based model. Both versions leverage a careful initialization of the parameters, the denoising effect of language models and automatic generation of parallel data by iterative back-translation. These models are significantly better than methods from the literature, while being simpler and having fewer hyper-parameters. On the widely used WMT’14 English-French and WMT’16 German-English benchmarks, our models respectively obtain 28.1 and 25.2 BLEU points without using a single parallel sentence, outperforming the state of the art by more than 11 BLEU points. On low-resource languages like English-Urdu and English-Romanian, our methods achieve even better results than semi-supervised and supervised approaches leveraging the paucity of available bitexts. Our code for NMT and PBSMT is publicly available.

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Classical Structured Prediction Losses for Sequence to Sequence Learning
Sergey Edunov | Myle Ott | Michael Auli | David Grangier | Marc’Aurelio Ranzato
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

There has been much recent work on training neural attention models at the sequence-level using either reinforcement learning-style methods or by optimizing the beam. In this paper, we survey a range of classical objective functions that have been widely used to train linear models for structured prediction and apply them to neural sequence to sequence models. Our experiments show that these losses can perform surprisingly well by slightly outperforming beam search optimization in a like for like setup. We also report new state of the art results on both IWSLT’14 German-English translation as well as Gigaword abstractive summarization. On the large WMT’14 English-French task, sequence-level training achieves 41.5 BLEU which is on par with the state of the art.

2014

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Linguistic Models of Deceptive Opinion Spam
Myle Ott
Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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Towards a General Rule for Identifying Deceptive Opinion Spam
Jiwei Li | Myle Ott | Claire Cardie | Eduard Hovy
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

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Identifying Manipulated Offerings on Review Portals
Jiwei Li | Myle Ott | Claire Cardie
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Negative Deceptive Opinion Spam
Myle Ott | Claire Cardie | Jeffrey T. Hancock
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2012

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Proceedings of the NAACL HLT 2012 Student Research Workshop
Rivka Levitan | Myle Ott | Roger Levy | Ani Nenkova
Proceedings of the NAACL HLT 2012 Student Research Workshop

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In Search of a Gold Standard in Studies of Deception
Stephanie Gokhman | Jeff Hancock | Poornima Prabhu | Myle Ott | Claire Cardie
Proceedings of the Workshop on Computational Approaches to Deception Detection

2011

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Finding Deceptive Opinion Spam by Any Stretch of the Imagination
Myle Ott | Yejin Choi | Claire Cardie | Jeffrey T. Hancock
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies