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WeChat Neural Machine Translation Systems for WMT20

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arxiv 2010.00247 v2 pith:KTCNY6DD submitted 2020-10-01 cs.CL cs.AIcs.LG

WeChat Neural Machine Translation Systems for WMT20

Fandong Meng , Jianhao Yan , Yijin Liu , Yuan Gao , Xianfeng Zeng , Qinsong Zeng , Peng Li , Ming Chen
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Jie Zhou Sifan Liu Hao Zhou
This is my paper
classification cs.CL cs.AIcs.LG
keywords approacheschinesedataenglishknowledgesystemtranslationachieves
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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