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arXiv · 1906.10511

Benchmarking Neural Machine Translation for Southern African Languages

Laura Martinus·Jade Z. Abbott
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Abstract

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.

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Laura Martinus, Jade Z. Abbott. 2019-06-17. Benchmarking Neural Machine Translation for Southern African Languages. https://arxiv.org/abs/1906.10511

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