WeChat Neural Machine Translation Systems for WMT21
Xianfeng Zeng, Yijin Liu, Ernan Li, Qiu Ran, Fandong Meng, Peng Li, Jinan Xu, Jie Zhou
Abstract
This paper introduces WeChat AI’s participation in WMT 2021 shared news translation task on English->Chinese, English->Japanese, Japanese->English and English->German. Our systems are based on the Transformer (Vaswani et al., 2017) with several novel and effective variants. In our experiments, we employ data filtering, large-scale synthetic data generation (i.e., back-translation, knowledge distillation, forward-translation, iterative in-domain knowledge transfer), advanced finetuning approaches, and boosted Self-BLEU based model ensemble. Our constrained systems achieve 36.9, 46.9, 27.8 and 31.3 case-sensitive BLEU scores on English->Chinese, English->Japanese, Japanese->English and English->German, respectively. The BLEU scores of English->Chinese, English->Japanese and Japanese->English are the highest among all submissions, and that of English->German is the highest among all constrained submissions.- Anthology ID:
- 2021.wmt-1.23
- Volume:
- Proceedings of the Sixth Conference on Machine Translation
- Month:
- November
- Year:
- 2021
- Address:
- Online
- Editors:
- Loic Barrault, Ondrej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussa, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Tom Kocmi, Andre Martins, Makoto Morishita, Christof Monz
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 243–254
- Language:
- URL:
- https://aclanthology.org/2021.wmt-1.23
- DOI:
- Bibkey:
- Cite (ACL):
- Xianfeng Zeng, Yijin Liu, Ernan Li, Qiu Ran, Fandong Meng, Peng Li, Jinan Xu, and Jie Zhou. 2021. WeChat Neural Machine Translation Systems for WMT21. In Proceedings of the Sixth Conference on Machine Translation, pages 243–254, Online. Association for Computational Linguistics.
- Cite (Informal):
- WeChat Neural Machine Translation Systems for WMT21 (Zeng et al., WMT 2021)
- Copy Citation:
- PDF:
- https://aclanthology.org/2021.wmt-1.23.pdf
Export citation
@inproceedings{zeng-etal-2021-wechat,
title = "{W}e{C}hat Neural Machine Translation Systems for {WMT}21",
author = "Zeng, Xianfeng and
Liu, Yijin and
Li, Ernan and
Ran, Qiu and
Meng, Fandong and
Li, Peng and
Xu, Jinan and
Zhou, Jie",
editor = "Barrault, Loic and
Bojar, Ondrej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-jussa, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Kocmi, Tom and
Martins, Andre and
Morishita, Makoto and
Monz, Christof",
booktitle = "Proceedings of the Sixth Conference on Machine Translation",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wmt-1.23",
pages = "243--254",
abstract = "This paper introduces WeChat AI{'}s participation in WMT 2021 shared news translation task on English-{\textgreater}Chinese, English-{\textgreater}Japanese, Japanese-{\textgreater}English and English-{\textgreater}German. Our systems are based on the Transformer (Vaswani et al., 2017) with several novel and effective variants. In our experiments, we employ data filtering, large-scale synthetic data generation (i.e., back-translation, knowledge distillation, forward-translation, iterative in-domain knowledge transfer), advanced finetuning approaches, and boosted Self-BLEU based model ensemble. Our constrained systems achieve 36.9, 46.9, 27.8 and 31.3 case-sensitive BLEU scores on English-{\textgreater}Chinese, English-{\textgreater}Japanese, Japanese-{\textgreater}English and English-{\textgreater}German, respectively. The BLEU scores of English-{\textgreater}Chinese, English-{\textgreater}Japanese and Japanese-{\textgreater}English are the highest among all submissions, and that of English-{\textgreater}German is the highest among all constrained submissions.",
}
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%0 Conference Proceedings %T WeChat Neural Machine Translation Systems for WMT21 %A Zeng, Xianfeng %A Liu, Yijin %A Li, Ernan %A Ran, Qiu %A Meng, Fandong %A Li, Peng %A Xu, Jinan %A Zhou, Jie %Y Barrault, Loic %Y Bojar, Ondrej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussa, Marta R. %Y Federmann, Christian %Y Fishel, Mark %Y Fraser, Alexander %Y Freitag, Markus %Y Graham, Yvette %Y Grundkiewicz, Roman %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Kocmi, Tom %Y Martins, Andre %Y Morishita, Makoto %Y Monz, Christof %S Proceedings of the Sixth Conference on Machine Translation %D 2021 %8 November %I Association for Computational Linguistics %C Online %F zeng-etal-2021-wechat %X This paper introduces WeChat AI’s participation in WMT 2021 shared news translation task on English-\textgreaterChinese, English-\textgreaterJapanese, Japanese-\textgreaterEnglish and English-\textgreaterGerman. Our systems are based on the Transformer (Vaswani et al., 2017) with several novel and effective variants. In our experiments, we employ data filtering, large-scale synthetic data generation (i.e., back-translation, knowledge distillation, forward-translation, iterative in-domain knowledge transfer), advanced finetuning approaches, and boosted Self-BLEU based model ensemble. Our constrained systems achieve 36.9, 46.9, 27.8 and 31.3 case-sensitive BLEU scores on English-\textgreaterChinese, English-\textgreaterJapanese, Japanese-\textgreaterEnglish and English-\textgreaterGerman, respectively. The BLEU scores of English-\textgreaterChinese, English-\textgreaterJapanese and Japanese-\textgreaterEnglish are the highest among all submissions, and that of English-\textgreaterGerman is the highest among all constrained submissions. %U https://aclanthology.org/2021.wmt-1.23 %P 243-254
Markdown (Informal)
[WeChat Neural Machine Translation Systems for WMT21](https://aclanthology.org/2021.wmt-1.23) (Zeng et al., WMT 2021)
- WeChat Neural Machine Translation Systems for WMT21 (Zeng et al., WMT 2021)
ACL
- Xianfeng Zeng, Yijin Liu, Ernan Li, Qiu Ran, Fandong Meng, Peng Li, Jinan Xu, and Jie Zhou. 2021. WeChat Neural Machine Translation Systems for WMT21. In Proceedings of the Sixth Conference on Machine Translation, pages 243–254, Online. Association for Computational Linguistics.