The DeepMind Chinese–English Document Translation System at WMT2020 (2024)

@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.