Domain-specific MT for Low-resource Languages: The case of Bambara-French

Allahsera Auguste Tapo, Michael Leventhal, Sarah Luger, Christopher M. Homan, Marcos Zampieri

Translating to and from low-resource languages is a challenge for machine translation (MT) systems due to a lack of parallel data. In this paper we address the issue of domain-specific MT for Bambara, an under-resourced Mande language spoken in Mali. We present the first domain-specific parallel dataset for MT of Bambara into and from French. We discuss challenges in working with small quantities of domain-specific data for a low-resource language and we present the results of machine learning experiments on this data.

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