The NiuTrans Machine Translation Systems for WMT20

Yuhao Zhang, Ziyang Wang, Runzhe Cao, Binghao Wei, Weiqiao Shan, Shuhan Zhou, Abudurexiti Reheman, Tao Zhou, Xin Zeng, Laohu Wang, Yongyu Mu, Jingnan Zhang, Xiaoqian Liu, Xuanjun Zhou, Yinqiao Li, Bei Li, Tong Xiao, Jingbo Zhu


Abstract
This paper describes NiuTrans neural machine translation systems of the WMT20 news translation tasks. We participated in Japanese<->English, English->Chinese, Inuktitut->English and Tamil->English total five tasks and rank first in Japanese<->English both sides. We mainly utilized iterative back-translation, different depth and widen model architectures, iterative knowledge distillation and iterative fine-tuning. And we find that adequately widened and deepened the model simultaneously, the performance will significantly improve. Also, iterative fine-tuning strategy we implemented is effective during adapting domain. For Inuktitut->English and Tamil->English tasks, we built multilingual models separately and employed pretraining word embedding to obtain better performance.
Anthology ID:
2020.wmt-1.37
Volume:
Proceedings of the Fifth Conference on Machine Translation
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | WMT
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
338–345
Language:
URL:
https://aclanthology.org/2020.wmt-1.37
DOI:
Bibkey:
Cite (ACL):
Yuhao Zhang, Ziyang Wang, Runzhe Cao, Binghao Wei, Weiqiao Shan, Shuhan Zhou, Abudurexiti Reheman, Tao Zhou, Xin Zeng, Laohu Wang, Yongyu Mu, Jingnan Zhang, Xiaoqian Liu, Xuanjun Zhou, Yinqiao Li, Bei Li, Tong Xiao, and Jingbo Zhu. 2020. The NiuTrans Machine Translation Systems for WMT20. In Proceedings of the Fifth Conference on Machine Translation, pages 338–345, Online. Association for Computational Linguistics.
Cite (Informal):
The NiuTrans Machine Translation Systems for WMT20 (Zhang et al., WMT 2020)
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PDF:
https://aclanthology.org/2020.wmt-1.37.pdf
Video:
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