This paper describes Tencent's multilingual machine translation systems for
the WMT22 shared task on Large-Scale Machine Translation Evaluation for African
Languages. We participated in the $\mathbf{constrained}$ translation track in
which only the data and pretrained models provided by the organizer are
allowed. The task is challenging due to three problems, including the absence
of training data for some to-be-evaluated language pairs, the uneven
optimization of language pairs caused by data imbalance, and the curse of
multilinguality. To address these problems, we adopt data augmentation,
distributionally robust optimization, and language family grouping,
respectively, to develop our multilingual neural machine translation (MNMT)
models. Our submissions won the $\mathbf{1st\ place}$ on the blind test sets in
terms of the automatic evaluation metrics. Codes, models, and detailed
competition results are available at
this https URL.

本文介绍了腾讯的多语言机器翻译系统，该系统使用数据扩增、分布式稳健优化和语系分组等技术来应对数据不平衡和多语言难题，其中在 WMT22 的有限数据情况下，取得了第一名的成绩。