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This paper describes Tencent's multilingual machine translation systems forthe WMT22 shared task on Large-Scale Machine Translation Evaluation for AfricanLanguages. We participated in the $\mathbf{constrained}$ translation track inwhich only the data and pretrained models provided by the organizer areallowed. The task is challenging due to three problems, including the absenceof training data for some to-be-evaluated language pairs, the unevenoptimization of language pairs caused by data imbalance, and the curse ofmultilinguality. 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 interms of the automatic evaluation metrics. Codes, models, and detailedcompetition results are available athttps://github.com/wxjiao/WMT2022-Large-Scale-African.

Tencent's Multilingual Machine Translation System for WMT22 Large-Scale African Languages