@inproceedings{meng-etal-2020-wechat,
title = "{W}e{C}hat Neural Machine Translation Systems for {WMT}20",
author = "Meng, Fandong and
Yan, Jianhao and
Liu, Yijin and
Gao, Yuan and
Zeng, Xianfeng and
Zeng, Qinsong and
Li, Peng and
Chen, Ming and
Zhou, Jie and
Liu, Sifan and
Zhou, Hao",
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-1.24",
pages = "239--247",
abstract = "We participate in the WMT 2020 shared newstranslation task on Chinese→English. Our system is based on the Transformer (Vaswaniet al., 2017a) with effective variants and the DTMT (Meng and Zhang, 2019) architecture. In our experiments, we employ data selection, several synthetic data generation approaches (i.e., back-translation, knowledge distillation, and iterative in-domain knowledge transfer), advanced finetuning approaches and self-bleu based model ensemble. Our constrained Chinese→English system achieves 36.9 case-sensitive BLEU score, which is thehighest among all submissions.",
}
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<abstract>We participate in the WMT 2020 shared newstranslation task on Chinese→English. Our system is based on the Transformer (Vaswaniet al., 2017a) with effective variants and the DTMT (Meng and Zhang, 2019) architecture. In our experiments, we employ data selection, several synthetic data generation approaches (i.e., back-translation, knowledge distillation, and iterative in-domain knowledge transfer), advanced finetuning approaches and self-bleu based model ensemble. Our constrained Chinese→English system achieves 36.9 case-sensitive BLEU score, which is thehighest among all submissions.</abstract>
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%0 Conference Proceedings
%T WeChat Neural Machine Translation Systems for WMT20
%A Meng, Fandong
%A Yan, Jianhao
%A Liu, Yijin
%A Gao, Yuan
%A Zeng, Xianfeng
%A Zeng, Qinsong
%A Li, Peng
%A Chen, Ming
%A Zhou, Jie
%A Liu, Sifan
%A Zhou, Hao
%S Proceedings of the Fifth Conference on Machine Translation
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F meng-etal-2020-wechat
%X We participate in the WMT 2020 shared newstranslation task on Chinese→English. Our system is based on the Transformer (Vaswaniet al., 2017a) with effective variants and the DTMT (Meng and Zhang, 2019) architecture. In our experiments, we employ data selection, several synthetic data generation approaches (i.e., back-translation, knowledge distillation, and iterative in-domain knowledge transfer), advanced finetuning approaches and self-bleu based model ensemble. Our constrained Chinese→English system achieves 36.9 case-sensitive BLEU score, which is thehighest among all submissions.
%U https://aclanthology.org/2020.wmt-1.24
%P 239-247
Markdown (Informal)
[WeChat Neural Machine Translation Systems for WMT20](https://aclanthology.org/2020.wmt-1.24) (Meng et al., WMT 2020)
ACL
- Fandong Meng, Jianhao Yan, Yijin Liu, Yuan Gao, Xianfeng Zeng, Qinsong Zeng, Peng Li, Ming Chen, Jie Zhou, Sifan Liu, and Hao Zhou. 2020. WeChat Neural Machine Translation Systems for WMT20. In Proceedings of the Fifth Conference on Machine Translation, pages 239–247, Online. Association for Computational Linguistics.