WeChat Neural Machine Translation Systems for WMT20
Fandong Meng
Jianhao Yan
Yijin Liu
Yuan Gao
Xia Zeng
Qinsong Zeng
Peng Li
Ming Chen
Jie Zhou
Sifan Liu
Hao Zhou

Abstract
We participate in the WMT 2020 shared news translation task on Chinese to English. Our system is based on the Transformer (Vaswani et al., 2017a) with effective variants and the DTMT (Meng and Zhang, 2019) architecture. In our experiments, we employ data selection, several synthetic data generation approaches (i.e., back-translation, knowledge distillation, and iterative in-domain knowledge transfer), advanced finetuning approaches and self-bleu based model ensemble. Our constrained Chinese to English system achieves 36.9 case-sensitive BLEU score, which is the highest among all submissions.
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