M. Lewis

Publications63
h-index31
Citations14,791
Highly Influential Citations3,802
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RoBERTa: A Robustly Optimized BERT Pretraining Approach
TLDR
It is found that BERT was significantly undertrained, and can match or exceed the performance of every model published after it, and the best model achieves state-of-the-art results on GLUE, RACE and SQuAD.
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BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
TLDR
BART is presented, a denoising autoencoder for pretraining sequence-to-sequence models, which matches the performance of RoBERTa on GLUE and SQuAD, and achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks.
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Hierarchical Neural Story Generation
TLDR
This work collects a large dataset of 300K human-written stories paired with writing prompts from an online forum that enables hierarchical story generation, where the model first generates a premise, and then transforms it into a passage of text.
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Multilingual Denoising Pre-training for Neural Machine Translation
Abstract This paper demonstrates that multilingual denoising pre-training produces significant performance gains across a wide variety of machine translation (MT) tasks. We present mBART—a…
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End-to-end Neural Coreference Resolution
TLDR
This work introduces the first end-to-end coreference resolution model, trained to maximize the marginal likelihood of gold antecedent spans from coreference clusters and is factored to enable aggressive pruning of potential mentions.
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Deep Semantic Role Labeling: What Works and What's Next
We introduce a new deep learning model for semantic role labeling (SRL) that significantly improves the state of the art, along with detailed analyses to reveal its strengths and limitations. We use…
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Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
TLDR
A general-purpose fine-tuning recipe for retrieval-augmented generation (RAG) -- models which combine pre-trained parametric and non-parametric memory for language generation, and finds that RAG models generate more specific, diverse and factual language than a state-of-the-art parametric-only seq2seq baseline.
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Deal or No Deal? End-to-End Learning of Negotiation Dialogues
TLDR
For the first time, it is shown it is possible to train end-to-end models for negotiation, which must learn both linguistic and reasoning skills with no annotated dialogue states, and this technique dramatically improves performance.
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Asking and Answering Questions to Evaluate the Factual Consistency of Summaries
TLDR
QAGS (pronounced “kags”), an automatic evaluation protocol that is designed to identify factual inconsistencies in a generated summary, is proposed and is believed to be a promising tool in automatically generating usable and factually consistent text.
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Cross-lingual Transfer Learning for Multilingual Task Oriented Dialog
TLDR
This paper presents a new data set of 57k annotated utterances in English, Spanish, Spanish and Thai and uses this data set to evaluate three different cross-lingual transfer methods, finding that given several hundred training examples in the the target language, the latter two methods outperform translating the training data.
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