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Statistical machine translation for dialectal Arabic is characterized by alack of data since data acquisition involves the transcription and translationof spoken language. In this study we develop techniques for extracting paralleldata for one particular dialect of Arabic (Iraqi Arabic) from out-of-domaincorpora in different dialects of Arabic or in Modern Standard Arabic. Wecompare two different data selection strategies (cross-entropy based andsubmodular selection) and demonstrate that a very small but highly targetedamount of found data can improve the performance of a baseline machinetranslation system. We furthermore report on preliminary experiments on usingautomatically translated speech data as additional training data.

Exploiting Out-of-Domain Data Sources for Dialectal Arabic Statistical Machine Translation