The DeepMind Chinese–English Document Translation System at WMT2020
Lei Yu, Laurent Sartran, Po-Sen Huang, Wojciech Stokowiec, Domenic Donato, Srivatsan Srinivasan, Alek Andreev, Wang Ling, Sona Mokra, Agustin Dal Lago, Yotam Doron, Susannah Young, Phil Blunsom, Chris Dyer
Abstract
This paper describes the DeepMind submission to the Chinese→English constrained data track of the WMT2020 Shared Task on News Translation. The submission employs a noisy channel factorization as the backbone of a document translation system. This approach allows the flexible combination of a number of independent component models which are further augmented with back-translation, distillation, fine-tuning with in-domain data, Monte-Carlo Tree Search decoding, and improved uncertainty estimation. In order to address persistent issues with the premature truncation of long sequences we included specialized length models and sentence segmentation techniques. Our final system provides a 9.9 BLEU points improvement over a baseline Transformer on our test set (newstest 2019).- Anthology ID:
- 2020.wmt-1.36
- Volume:
- Proceedings of the Fifth Conference on Machine Translation
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Yvette Graham, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 326–337
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.36
- DOI:
- Bibkey:
- Cite (ACL):
- Lei Yu, Laurent Sartran, Po-Sen Huang, Wojciech Stokowiec, Domenic Donato, Srivatsan Srinivasan, Alek Andreev, Wang Ling, Sona Mokra, Agustin Dal Lago, Yotam Doron, Susannah Young, Phil Blunsom, and Chris Dyer. 2020. The DeepMind Chinese–English Document Translation System at WMT2020. In Proceedings of the Fifth Conference on Machine Translation, pages 326–337, Online. Association for Computational Linguistics.
- Cite (Informal):
- The DeepMind Chinese–English Document Translation System at WMT2020 (Yu et al., WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.36.pdf
- Video:
- https://slideslive.com/38939586
Export citation
@inproceedings{yu-etal-2020-deepmind,
title = "The {D}eep{M}ind {C}hinese{--}{E}nglish Document Translation System at {WMT}2020",
author = "Yu, Lei and
Sartran, Laurent and
Huang, Po-Sen and
Stokowiec, Wojciech and
Donato, Domenic and
Srinivasan, Srivatsan and
Andreev, Alek and
Ling, Wang and
Mokra, Sona and
Dal Lago, Agustin and
Doron, Yotam and
Young, Susannah and
Blunsom, Phil and
Dyer, Chris",
editor = {Barrault, Lo{\"\i}c and
Bojar, Ond{\v{r}}ej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss{\`a}, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Graham, Yvette and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr{\'e} and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.36",
pages = "326--337",
abstract = "This paper describes the DeepMind submission to the Chinese$\rightarrow$English constrained data track of the WMT2020 Shared Task on News Translation. The submission employs a noisy channel factorization as the backbone of a document translation system. This approach allows the flexible combination of a number of independent component models which are further augmented with back-translation, distillation, fine-tuning with in-domain data, Monte-Carlo Tree Search decoding, and improved uncertainty estimation. In order to address persistent issues with the premature truncation of long sequences we included specialized length models and sentence segmentation techniques. Our final system provides a 9.9 BLEU points improvement over a baseline Transformer on our test set (newstest 2019).",
}
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%0 Conference Proceedings %T The DeepMind Chinese–English Document Translation System at WMT2020 %A Yu, Lei %A Sartran, Laurent %A Huang, Po-Sen %A Stokowiec, Wojciech %A Donato, Domenic %A Srinivasan, Srivatsan %A Andreev, Alek %A Ling, Wang %A Mokra, Sona %A Dal Lago, Agustin %A Doron, Yotam %A Young, Susannah %A Blunsom, Phil %A Dyer, Chris %Y Barrault, Loïc %Y Bojar, Ondřej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussà, Marta R. %Y Federmann, Christian %Y Fishel, Mark %Y Fraser, Alexander %Y Graham, Yvette %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Martins, André %Y Morishita, Makoto %Y Monz, Christof %Y Nagata, Masaaki %Y Nakazawa, Toshiaki %Y Negri, Matteo %S Proceedings of the Fifth Conference on Machine Translation %D 2020 %8 November %I Association for Computational Linguistics %C Online %F yu-etal-2020-deepmind %X This paper describes the DeepMind submission to the Chinese\rightarrowEnglish constrained data track of the WMT2020 Shared Task on News Translation. The submission employs a noisy channel factorization as the backbone of a document translation system. This approach allows the flexible combination of a number of independent component models which are further augmented with back-translation, distillation, fine-tuning with in-domain data, Monte-Carlo Tree Search decoding, and improved uncertainty estimation. In order to address persistent issues with the premature truncation of long sequences we included specialized length models and sentence segmentation techniques. Our final system provides a 9.9 BLEU points improvement over a baseline Transformer on our test set (newstest 2019). %U https://aclanthology.org/2020.wmt-1.36 %P 326-337
Markdown (Informal)
[The DeepMind Chinese–English Document Translation System at WMT2020](https://aclanthology.org/2020.wmt-1.36) (Yu et al., WMT 2020)
- The DeepMind Chinese–English Document Translation System at WMT2020 (Yu et al., WMT 2020)
ACL
- Lei Yu, Laurent Sartran, Po-Sen Huang, Wojciech Stokowiec, Domenic Donato, Srivatsan Srinivasan, Alek Andreev, Wang Ling, Sona Mokra, Agustin Dal Lago, Yotam Doron, Susannah Young, Phil Blunsom, and Chris Dyer. 2020. The DeepMind Chinese–English Document Translation System at WMT2020. In Proceedings of the Fifth Conference on Machine Translation, pages 326–337, Online. Association for Computational Linguistics.