Jingnan Zhang


2021

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The NiuTrans Machine Translation Systems for WMT21
Shuhan Zhou | Tao Zhou | Binghao Wei | Yingfeng Luo | Yongyu Mu | Zefan Zhou | Chenglong Wang | Xuanjun Zhou | Chuanhao Lv | Yi Jing | Laohu Wang | Jingnan Zhang | Canan Huang | Zhongxiang Yan | Chi Hu | Bei Li | Tong Xiao | Jingbo Zhu
Proceedings of the Sixth Conference on Machine Translation

This paper describes NiuTrans neural machine translation systems of the WMT 2021 news translation tasks. We made submissions to 9 language directions, including English2Chinese, Japanese, Russian, Icelandic and English2Hausa tasks. Our primary systems are built on several effective variants of Transformer, e.g., Transformer-DLCL, ODE-Transformer. We also utilize back-translation, knowledge distillation, post-ensemble, and iterative fine-tuning techniques to enhance the model performance further.

2020

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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.