Search Results for author: Sergey Edunov

Found 19 papers, 12 papers with code

Facebook AI WMT21 News Translation Task Submission

no code implementations6 Aug 2021 Chau Tran, Shruti Bhosale, James Cross, Philipp Koehn, Sergey Edunov, Angela Fan

We describe Facebook's multilingual model submission to the WMT2021 shared task on news translation.


Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling

1 code implementation WMT (EMNLP) 2020 Shruti Bhosale, Kyra Yee, Sergey Edunov, Michael Auli

Pre-training models on vast quantities of unlabeled data has emerged as an effective approach to improving accuracy on many NLP tasks.

 Ranked #1 on Machine Translation on WMT2016 Romanian-English (using extra training data)

Machine Translation Translation

Beyond English-Centric Multilingual Machine Translation

4 code implementations21 Oct 2020 Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin

Existing work in translation demonstrated the potential of massively multilingual machine translation by training a single model able to translate between any pair of languages.

Machine Translation Translation

Large scale weakly and semi-supervised learning for low-resource video ASR

no code implementations16 May 2020 Kritika Singh, Vimal Manohar, Alex Xiao, Sergey Edunov, Ross Girshick, Vitaliy Liptchinsky, Christian Fuegen, Yatharth Saraf, Geoffrey Zweig, Abdel-rahman Mohamed

Many semi- and weakly-supervised approaches have been investigated for overcoming the labeling cost of building high quality speech recognition systems.

Speech Recognition

Dense Passage Retrieval for Open-Domain Question Answering

11 code implementations EMNLP 2020 Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih

Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method.

Open-Domain Question Answering Passage Retrieval

Multilingual Denoising Pre-training for Neural Machine Translation

5 code implementations22 Jan 2020 Yinhan Liu, Jiatao Gu, Naman Goyal, Xi-An Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer

This paper demonstrates that multilingual denoising pre-training produces significant performance gains across a wide variety of machine translation (MT) tasks.

Denoising Translation +1

CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB

3 code implementations ACL 2021 Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave, Armand Joulin

To evaluate the quality of the mined bitexts, we train NMT systems for most of the language pairs and evaluate them on TED, WMT and WAT test sets.


Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP

no code implementations ICLR 2020 Haonan Yu, Sergey Edunov, Yuandong Tian, Ari S. Morcos

The lottery ticket hypothesis proposes that over-parameterization of deep neural networks (DNNs) aids training by increasing the probability of a "lucky" sub-network initialization being present rather than by helping the optimization process (Frankle & Carbin, 2019).

Image Classification

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

Cloze-driven Pretraining of Self-attention Networks

no code implementations IJCNLP 2019 Alexei Baevski, Sergey Edunov, Yinhan Liu, Luke Zettlemoyer, Michael Auli

We present a new approach for pretraining a bi-directional transformer model that provides significant performance gains across a variety of language understanding problems.

Constituency Parsing Named Entity Recognition +3

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

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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