African languages are numerous, complex and low-resourced. The datasets
required for machine translation are difficult to discover, and existing
research is hard to reproduce. Minimal attention has been given to machine
translation for African languages so there is scant research regarding the
problems that arise when using machine translation techniques. To begin
addressing these problems, we trained models to translate English to five of
the official South African languages (Afrikaans, isiZulu, Northern Sotho,
Setswana, Xitsonga), making use of modern neural machine translation
techniques. The results obtained show the promise of using neural machine
translation techniques for African languages. By providing reproducible
publicly-available data, code and results, this research aims to provide a
starting point for other researchers in African machine translation to compare
to and build upon.

探讨使用现代神经机器翻译技术实现英语翻译成五种南非官方语言（Afrikaans、isiZulu、Northern Sotho、Setswana、Xitsonga），提供可重复使用的数据、代码和结果，为非洲机器翻译研究提供比较和承建的起点。