  Statistical machine translation for dialectal Arabic is characterized by a
lack of data since data acquisition involves the transcription and translation
of spoken language. In this study we develop techniques for extracting parallel
data for one particular dialect of Arabic (Iraqi Arabic) from out-of-domain
corpora in different dialects of Arabic or in Modern Standard Arabic. We
compare two different data selection strategies (cross-entropy based and
submodular selection) and demonstrate that a very small but highly targeted
amount of found data can improve the performance of a baseline machine
translation system. We furthermore report on preliminary experiments on using
automatically translated speech data as additional training data.
