  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.
