Xuanjun Zhou
2021
The NiuTrans Machine Translation Systems for WMT21
Shuhan Zhou
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Tao Zhou
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Binghao Wei
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Yingfeng Luo
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Yongyu Mu
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Zefan Zhou
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Chenglong Wang
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Xuanjun Zhou
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Chuanhao Lv
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Yi Jing
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Laohu Wang
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Jingnan Zhang
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Canan Huang
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Zhongxiang Yan
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Chi Hu
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Bei Li
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Tong Xiao
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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
The NiuTrans Machine Translation Systems for WMT20
Yuhao Zhang
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Ziyang Wang
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Runzhe Cao
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Binghao Wei
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Weiqiao Shan
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Shuhan Zhou
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Abudurexiti Reheman
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Tao Zhou
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Xin Zeng
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Laohu Wang
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Yongyu Mu
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Jingnan Zhang
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Xiaoqian Liu
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Xuanjun Zhou
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Yinqiao Li
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Bei Li
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Tong Xiao
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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.
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Co-authors
- Binghao Wei 2
- Shuhan Zhou 2
- Tao Zhou 2
- Laohu Wang 2
- Yongyu Mu 2
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Venues
- wmt2