Domain-specific MT for Low-resource Languages: The case of Bambara-French
This work addresses the challenge of domain-specific translation for speakers and users of Bambara, an under-resourced language, though it appears incremental as it focuses on dataset creation without major methodological breakthroughs.
The paper tackled the problem of domain-specific machine translation for the low-resource language Bambara by creating the first parallel dataset for Bambara-French translation and conducting experiments, but no concrete numerical results are provided in the abstract.
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.