  We present research towards bridging the language gap between migrant workers
in Qatar and medical staff. In particular, we present the first steps towards
the development of a real-world Hindi-English machine translation system for
doctor-patient communication. As this is a low-resource language pair,
especially for speech and for the medical domain, our initial focus has been on
gathering suitable training data from various sources. We applied a variety of
methods ranging from fully automatic extraction from the Web to manual
annotation of test data. Moreover, we developed a method for automatically
augmenting the training data with synthetically generated variants, which
yielded a very sizable improvement of more than 3 BLEU points absolute.
