  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.
