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

Code

• github.com/WalePhenomenon/Hausa-NMT
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Abstract

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.

Tasks

Reproductions