The paper describes the University of Cape Town's submission to the
constrained track of the WMT22 Shared Task: Large-Scale Machine Translation
Evaluation for African Languages. Our system is a single multilingual
translation model that translates between English and 8 South / South East
African Languages, as well as between specific pairs of the African languages.
We used several techniques suited for low-resource machine translation (MT),
including overlap BPE, back-translation, synthetic training data generation,
and adding more translation directions during training. Our results show the
value of these techniques, especially for directions where very little or no
bilingual training data is available.

该论文讲述了开普敦大学提交给 WMT22 大规模机器翻译评估竞赛的受限跟踪的情况。该系统是一个单一的多语种翻译模型，可在英语和 8 种南部 / 东南部非洲语言之间进行翻译，并可在特定的非洲语言对之间进行翻译。该研究采用了多种适用于低资源机器翻译的技术，包括重叠 BPE、回译、合成训练数据生成以及在训练过程中增加更多的翻译方向。结果表明，这些技术的价值尤为突出，特别是对于非常少或没有双语训练数据可用的方向。