Benchmarking Neural Machine Translation for Southern African Languages
This work addresses the problem of limited and scattered resources for African languages, enabling more accessible and reproducible machine translation research in this domain.
The authors tackled the lack of reproducible benchmarks for low-resourced Southern African languages by training neural machine translation models on publicly-available datasets for 5 languages, providing code and a new evaluation set to facilitate future research.
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.