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Towards Neural Machine Translation for African Languages

Domain:

natural language processing

Record type:

paper
Creator:
AbbMar
Publisher:
arXiv
Host:avatar
Given that South African education is in crisis, strategies for improvement and sustainability of high-quality, up-to-date education must be explored. In the migration of education online, inclusion of machine translation for low-resourced local languages becomes necessary. This paper aims to spur the use of current neural machine translation (NMT) techniques for low-resourced local languages. The paper demonstrates state-of-the-art performance on English-to-Setswana translation using the Autshumato dataset. The use of the Transformer architecture beat previous techniques by 5.33 BLEU points. This demonstrates the promise of using current NMT techniques for African languages.

Visit

doi.orgarxiv.org

Tasks

machine translation

Languages

Setswana

Tags

Computation and Language (cs.CL)Machine Learning (cs.LG)Machine Learning (stat.ML)FOS: Computer and information sciencesFOS: Computer and information sciences

Licenses

arXiv.org perpetual, non-exclusive licensehttp://arxiv.org/licenses/nonexclusive-distrib/1.0/

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