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WeChat Neural Machine Translation Systems for WMT20 (2010.00247v2)
Published 1 Oct 2020 in cs.CL, cs.AI, and cs.LG
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.
- Fandong Meng (174 papers)
- Jianhao Yan (27 papers)
- Yijin Liu (29 papers)
- Yuan Gao (336 papers)
- Xianfeng Zeng (5 papers)
- Qinsong Zeng (4 papers)
- Peng Li (390 papers)
- Ming Chen (124 papers)
- Jie Zhou (687 papers)
- Sifan Liu (28 papers)
- Hao Zhou (351 papers)