Benchmarking Neural Machine Translation for Southern African Languages

Jade Abbott, Laura Martinus


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, meaning researchers struggle to reproduce reported results, and almost no publicly available benchmarks or leaderboards for African machine translation models exist. To start to address these problems, we trained neural machine translation models for a subset of Southern African languages on publicly-available datasets. We provide the code for training the models and evaluate the models on a newly released evaluation set, with the aim of starting a leaderboard for Southern African languages and spur future research in the field.
Anthology ID:
W19-3632
Volume:
Proceedings of the 2019 Workshop on Widening NLP
Month:
August
Year:
2019
Address:
Florence, Italy
Editors:
Amittai Axelrod, Diyi Yang, Rossana Cunha, Samira Shaikh, Zeerak Waseem
Venue:
WiNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
98–101
Language:
URL:
https://aclanthology.org/W19-3632/
DOI:
Bibkey:
Cite (ACL):
Jade Abbott and Laura Martinus. 2019. Benchmarking Neural Machine Translation for Southern African Languages. In Proceedings of the 2019 Workshop on Widening NLP, pages 98–101, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Benchmarking Neural Machine Translation for Southern African Languages (Abbott & Martinus, WiNLP 2019)
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