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Given that South African education is in crisis, strategies for improvementand sustainability of high-quality, up-to-date education must be explored. Inthe migration of education online, inclusion of machine translation forlow-resourced local languages becomes necessary. This paper aims to spur theuse of current neural machine translation (NMT) techniques for low-resourcedlocal languages. The paper demonstrates state-of-the-art performance onEnglish-to-Setswana translation using the Autshumato dataset. The use of theTransformer architecture beat previous techniques by 5.33 BLEU points. Thisdemonstrates the promise of using current NMT techniques for African languages.

Towards Neural Machine Translation for African Languages