Abstract
Recent advances in neural machine translation (NMT) have led to state-of-the-art results for many European-based translation tasks. However, despite these advances, there is has been little focus in applying these methods to African languages. In this paper, we seek to address this gap by creating an NMT benchmark BLEU score between English and the ten remaining official languages in South Africa.
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