Egyptian Arabic to English Statistical Machine Translation System for NIST OpenMT'2015
The paper describes the Egyptian Arabic to English statistical machine translation (SMT) system that the QCRI Columbia NYUAD (QCN) group submitted to the NIST OpenMT'2015 competition. The competition focused on informal dialectal Arabic, as used in SMS, chat, and speech. Thus, our efforts focused on processing and standardizing Arabic, e.g. using tools such as 3arrib and MADAMIRA. We further trained a phrase based SMT system using state of the art features and components such as operation sequence model, class based language model, sparse features, neural network joint model, genre based hierarchically interpolated language model, unsupervised transliteration mining, phrase table merging, and hypothesis combination. Our system ranked second on all three genres.
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