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The paper describes the Egyptian Arabic-to-English statistical machinetranslation (SMT) system that the QCRI-Columbia-NYUAD (QCN) group submitted tothe NIST OpenMT'2015 competition. The competition focused on informal dialectalArabic, as used in SMS, chat, and speech. Thus, our efforts focused onprocessing and standardizing Arabic, e.g., using tools such as 3arrib andMADAMIRA. We further trained a phrase-based SMT system using state-of-the-artfeatures and components such as operation sequence model, class-based languagemodel, sparse features, neural network joint model, genre-basedhierarchically-interpolated language model, unsupervised transliterationmining, phrase-table merging, and hypothesis combination. Our system rankedsecond on all three genres.

Egyptian Arabic to English Statistical Machine Translation System for NIST OpenMT'2015