论文标题
WMT20的微信神经机器翻译系统
WeChat Neural Machine Translation Systems for WMT20
论文作者
论文摘要
我们参加了WMT 2020共享中文的新闻翻译任务。我们的系统基于具有有效变体和DTMT（Meng and Zhang，2019）架构的变压器（Vaswani等，2017a）。在我们的实验中，我们采用数据选择，几种综合数据生成方法（即背面翻译，知识蒸馏和迭代内域知识转移），先进的鉴定方法和基于自我的模型集合。我们受到限制的英语系统达到了36.9个对案例敏感的BLEU得分，这是所有提交中最高的。
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.
