Search Results for author: Myle Ott

Found 39 papers, 19 papers with code

NormFormer: Improved Transformer Pretraining with Extra Normalization

1 code implementation18 Oct 2021 Sam Shleifer, Jason Weston, Myle Ott

The extra operations incur negligible compute cost (+0. 4% parameter increase), but improve pretraining perplexity and downstream task performance for both causal and masked language models ranging from 125 Million to 2. 7 Billion parameters.

Language Modelling

On Anytime Learning at Macroscale

no code implementations17 Jun 2021 Lucas Caccia, Jing Xu, Myle Ott, Marc'Aurelio Ranzato, Ludovic Denoyer

In this work, we consider such a streaming learning setting, which we dub anytime learning at macroscale} (ALMA).

Larger-Scale Transformers for Multilingual Masked Language Modeling

no code implementations ACL (RepL4NLP) 2021 Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau

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.

Language Modelling

Few-shot Sequence Learning with Transformers

no code implementations17 Dec 2020 Lajanugen Logeswaran, Ann Lee, Myle Ott, Honglak Lee, Marc'Aurelio Ranzato, Arthur Szlam

In the simplest setting, we append a token to an input sequence which represents the particular task to be undertaken, and show that the embedding of this token can be optimized on the fly given few labeled examples.

Few-Shot Learning

General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference

no code implementations Findings of the Association for Computational Linguistics 2020 Jingfei Du, Myle Ott, Haoran Li, Xing Zhou, Veselin Stoyanov

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.

Knowledge Distillation Quantization

Residual Energy-Based Models for Text Generation

1 code implementation ICLR 2020 Yuntian Deng, Anton Bakhtin, Myle Ott, Arthur Szlam, Marc'Aurelio Ranzato

In this work, we investigate un-normalized energy-based models (EBMs) which operate not at the token but at the sequence level.

Language Modelling Machine Translation +2

Residual Energy-Based Models for Text

no code implementations6 Apr 2020 Anton Bakhtin, Yuntian Deng, Sam Gross, Myle Ott, Marc'Aurelio Ranzato, Arthur Szlam

Current large-scale auto-regressive language models display impressive fluency and can generate convincing text.

Unsupervised Cross-lingual Representation Learning at Scale

22 code implementations ACL 2020 Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, Veselin Stoyanov

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.

Cross-Lingual Transfer Language Modelling +2

Analyzing the Forgetting Problem in the Pretrain-Finetuning of Dialogue Response Models

no code implementations16 Oct 2019 Tianxing He, Jun Liu, Kyunghyun Cho, Myle Ott, Bing Liu, James Glass, Fuchun Peng

We find that mix-review effectively regularizes the finetuning process, and the forgetting problem is alleviated to some extent.

Text Generation Transfer Learning

The Source-Target Domain Mismatch Problem in Machine Translation

no code implementations EACL 2021 Jiajun Shen, Peng-Jen Chen, Matt Le, Junxian He, Jiatao Gu, Myle Ott, Michael Auli, Marc'Aurelio Ranzato

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.

Machine Translation Translation

RoBERTa: A Robustly Optimized BERT Pretraining Approach

45 code implementations26 Jul 2019 Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov

Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.

Common Sense Reasoning Language Modelling +6

fairseq: A Fast, Extensible Toolkit for Sequence Modeling

5 code implementations NAACL 2019 Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli

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.

Language Modelling Text Generation +1

Phrase-Based \& Neural Unsupervised Machine Translation

no code implementations EMNLP 2018 Guillaume Lample, Myle Ott, Alexis Conneau, Ludovic Denoyer, Marc{'}Aurelio Ranzato

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.

Denoising Translation +1

Understanding Back-Translation at Scale

3 code implementations EMNLP 2018 Sergey Edunov, Myle Ott, Michael Auli, David Grangier

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.

Ranked #2 on Machine Translation on WMT2014 English-German (using extra training data)

Machine Translation Translation

Scaling Neural Machine Translation

5 code implementations WS 2018 Myle Ott, Sergey Edunov, David Grangier, Michael Auli

Sequence to sequence learning models still require several days to reach state of the art performance on large benchmark datasets using a single machine.

Machine Translation Question Answering +1

Phrase-Based & Neural Unsupervised Machine Translation

14 code implementations EMNLP 2018 Guillaume Lample, Myle Ott, Alexis Conneau, Ludovic Denoyer, Marc'Aurelio Ranzato

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.

Translation Unsupervised Machine Translation

Analyzing Uncertainty in Neural Machine Translation

1 code implementation ICML 2018 Myle Ott, Michael Auli, David Grangier, Marc'Aurelio Ranzato

We propose tools and metrics to assess how uncertainty in the data is captured by the model distribution and how it affects search strategies that generate translations.

Machine Translation Translation

Classical Structured Prediction Losses for Sequence to Sequence Learning

1 code implementation NAACL 2018 Sergey Edunov, Myle Ott, Michael Auli, David Grangier, Marc'Aurelio Ranzato

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.

Abstractive Text Summarization Machine Translation +2

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