  Unlike major Western languages, most African languages are very
low-resourced. Furthermore, the resources that do exist are often scattered and
difficult to obtain and discover. As a result, the data and code for existing
research has rarely been shared. This has lead a struggle to reproduce reported
results, and few publicly available benchmarks for African machine translation
models exist. To start to address these problems, we trained neural machine
translation models for 5 Southern African languages on publicly-available
datasets. Code is provided for training the models and evaluate the models on a
newly released evaluation set, with the aim of spur future research in the
field for Southern African languages.
