  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
https://github.com/wxjiao/WMT2022-Large-Scale-African.
