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
Proceedings of the Fifth Conference on Machine Translation
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.
- Yuhao Zhang 1
- Ziyang Wang 1
- Runzhe Cao 1
- Binghao Wei 1
- Weiqiao Shan 1
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