  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.
