Exploiting Out-of-Domain Data Sources for Dialectal Arabic Statistical Machine Translation
This addresses data scarcity for dialectal Arabic machine translation, but it is incremental as it builds on existing data selection methods.
The study tackled the lack of parallel data for Iraqi Arabic machine translation by extracting data from out-of-domain corpora in other Arabic dialects or Modern Standard Arabic, showing that a small, targeted amount of this data improved baseline system performance.
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.