@inproceedings{akinfaderin-2020-hausamt,
    title = "{H}ausa{MT} v1.0: Towards {E}nglish{--}{H}ausa Neural Machine Translation",
    author = "Akinfaderin, Adewale",
    editor = "Cunha, Rossana  and
      Shaikh, Samira  and
      Varis, Erika  and
      Georgi, Ryan  and
      Tsai, Alicia  and
      Anastasopoulos, Antonios  and
      Chandu, Khyathi Raghavi",
    booktitle = "Proceedings of the Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.winlp-1.38/",
    doi = "10.18653/v1/2020.winlp-1.38",
    pages = "144--147",
    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."
}