2021
EMNLP
EMNLP 2021
The NiuTrans Machine Translation Systems for WMT21
Abstract
AbstractThis 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.
🌉
Interdisciplinary Bridge
— Deep Learning and Natural Language Processing
🧭
Keyword Pioneer
— iterative fine tuning
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
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