  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.
