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.
@article{arxiv.1906.10511,
title = {Benchmarking Neural Machine Translation for Southern African Languages},
author = {Laura Martinus and Jade Z. Abbott},
journal= {arXiv preprint arXiv:1906.10511},
year = {2019}
}
Comments
arXiv admin note: text overlap with arXiv:1906.05685