Tencent Neural Machine Translation Systems for the WMT20 News Translation Task
Shuangzhi Wu, Xing Wang, Longyue Wang, Fangxu Liu, Jun Xie, Zhaopeng Tu, Shuming Shi, Mu Li
Abstract
This paper describes Tencent Neural Machine Translation systems for the WMT 2020 news translation tasks. We participate in the shared news translation task on English ↔ Chinese and English → German language pairs. Our systems are built on deep Transformer and several data augmentation methods. We propose a boosted in-domain finetuning method to improve single models. Ensemble is used to combine single models and we propose an iterative transductive ensemble method which can further improve the translation performance based on the ensemble results. We achieve a BLEU score of 36.8 and the highest chrF score of 0.648 on Chinese → English task.- Anthology ID:
- 2020.wmt-1.34
- 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:
- 313–319
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.34
- DOI:
- Bibkey:
- Cite (ACL):
- Shuangzhi Wu, Xing Wang, Longyue Wang, Fangxu Liu, Jun Xie, Zhaopeng Tu, Shuming Shi, and Mu Li. 2020. Tencent Neural Machine Translation Systems for the WMT20 News Translation Task. In Proceedings of the Fifth Conference on Machine Translation, pages 313–319, Online. Association for Computational Linguistics.
- Cite (Informal):
- Tencent Neural Machine Translation Systems for the WMT20 News Translation Task (Wu et al., WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.34.pdf
Export citation
@inproceedings{wu-etal-2020-tencent, title = "Tencent Neural Machine Translation Systems for the {WMT}20 News Translation Task", author = "Wu, Shuangzhi and Wang, Xing and Wang, Longyue and Liu, Fangxu and Xie, Jun and Tu, Zhaopeng and Shi, Shuming and Li, Mu", 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-1.34", pages = "313--319", abstract = "This paper describes Tencent Neural Machine Translation systems for the WMT 2020 news translation tasks. We participate in the shared news translation task on English $\leftrightarrow$ Chinese and English $\rightarrow$ German language pairs. Our systems are built on deep Transformer and several data augmentation methods. We propose a boosted in-domain finetuning method to improve single models. Ensemble is used to combine single models and we propose an iterative transductive ensemble method which can further improve the translation performance based on the ensemble results. We achieve a BLEU score of 36.8 and the highest chrF score of 0.648 on Chinese $\rightarrow$ English task.", }
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%0 Conference Proceedings %T Tencent Neural Machine Translation Systems for the WMT20 News Translation Task %A Wu, Shuangzhi %A Wang, Xing %A Wang, Longyue %A Liu, Fangxu %A Xie, Jun %A Tu, Zhaopeng %A Shi, Shuming %A Li, Mu %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 wu-etal-2020-tencent %X This paper describes Tencent Neural Machine Translation systems for the WMT 2020 news translation tasks. We participate in the shared news translation task on English łeftrightarrow Chinese and English \rightarrow German language pairs. Our systems are built on deep Transformer and several data augmentation methods. We propose a boosted in-domain finetuning method to improve single models. Ensemble is used to combine single models and we propose an iterative transductive ensemble method which can further improve the translation performance based on the ensemble results. We achieve a BLEU score of 36.8 and the highest chrF score of 0.648 on Chinese \rightarrow English task. %U https://aclanthology.org/2020.wmt-1.34 %P 313-319
Markdown (Informal)
[Tencent Neural Machine Translation Systems for the WMT20 News Translation Task](https://aclanthology.org/2020.wmt-1.34) (Wu et al., WMT 2020)
- Tencent Neural Machine Translation Systems for the WMT20 News Translation Task (Wu et al., WMT 2020)
ACL
- Shuangzhi Wu, Xing Wang, Longyue Wang, Fangxu Liu, Jun Xie, Zhaopeng Tu, Shuming Shi, and Mu Li. 2020. Tencent Neural Machine Translation Systems for the WMT20 News Translation Task. In Proceedings of the Fifth Conference on Machine Translation, pages 313–319, Online. Association for Computational Linguistics.