HausaMT v1.0: Towards English--Hausa Neural Machine Translation

Neural Machine Translation (NMT) for low-resource languages suffers from low performance because of the lack of large amounts of parallel data and language diversity. To contribute to ameliorating this problem, we built a baseline model for English{--}Hausa machine translation, which is considered a task for low{--}resource language. The Hausa language is the second largest Afro{--}Asiatic language in the world after Arabic and it is the third largest language for trading across a larger swath of West Africa countries, after English and French. In this paper, we curated different datasets containing Hausa{--}English parallel corpus for our translation. We trained baseline models and evaluated the performance of our models using the Recurrent and Transformer encoder{--}decoder architecture with two tokenization approaches: standard word{--}level tokenization and Byte Pair Encoding (BPE) subword tokenization.