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.

本研究介绍了 WeChat AI 在 WMT 2021 共享新闻翻译任务中的参与，并使用 Transformer 等多种方法来生成大量的合成数据，从而实现英语到中文、英语到日语、日语到英语和英语到德语的翻译，通过使用高级微调方法和基于 Self-BLEU 的模型加强，得到了 36.9、46.9、27.8 和 31.3 的 BLEU 分数，其中英语到中文、英语到日语和日语到英语的 BLEU 分数是所有提交中最高的，而英语到德语的 BLEU 分数是所有有限制的提交中最高的。