Wechat Neural Machine Translation Systems For WMT21

Zeng Xianfeng, Liu Yijin, Li Ernan, Ran Qiu, Meng Fandong, Li Peng, Xu Jinan, Zhou Jie. Arxiv 2021

[Paper]    
Applications Distillation Efficiency And Optimization Model Architecture Pretraining Methods Transformer

This paper introduces WeChat AI’s participation in WMT 2021 shared news translation task on English->Chinese, English->Japanese, Japanese->English and English->German. Our systems are based on the Transformer (Vaswani et al., 2017) with several novel and effective variants. In our experiments, we employ data filtering, large-scale synthetic data generation (i.e., back-translation, knowledge distillation, forward-translation, iterative in-domain knowledge transfer), advanced finetuning approaches, and boosted Self-BLEU based model ensemble. Our constrained systems achieve 36.9, 46.9, 27.8 and 31.3 case-sensitive BLEU scores on English->Chinese, English->Japanese, Japanese->English and English->German, respectively. The BLEU scores of English->Chinese, English->Japanese and Japanese->English are the highest among all submissions, and that of English->German is the highest among all constrained submissions.

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