Neural Machine Translation for South Africa's Official Languages
This addresses the problem of limited translation resources for South Africa's official languages, providing a foundational benchmark for future research, though it is incremental as it applies existing methods to new data.
The paper tackled the lack of neural machine translation (NMT) focus on African languages by creating an NMT benchmark with BLEU scores for English to ten South African official languages, establishing baseline results for these under-resourced languages.
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.