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.
Reference:
Abbott, J.Z. and Martinus, L.J.B. 2018. Towards neural machine translation for African languages. NIPS 2018 Workshop on Machine Learning for the Developing World, December 2018, Montreal, Canada
Abbott, J., & Martinus, L. J. (2018). Towards neural machine translation for African languages. http://hdl.handle.net/10204/10726
Abbott, JZ, and Laura JB Martinus. "Towards neural machine translation for African languages." (2018): http://hdl.handle.net/10204/10726
Abbott J, Martinus LJ, Towards neural machine translation for African languages; 2018. http://hdl.handle.net/10204/10726 .
