  Neural machine translation has become a major alternative to widely used
phrase-based statistical machine translation. We notice however that much of
research on neural machine translation has focused on European languages
despite its language agnostic nature. In this paper, we apply neural machine
translation to the task of Arabic translation (Ar<->En) and compare it against
a standard phrase-based translation system. We run extensive comparison using
various configurations in preprocessing Arabic script and show that the
phrase-based and neural translation systems perform comparably to each other
and that proper preprocessing of Arabic script has a similar effect on both of
the systems. We however observe that the neural machine translation
significantly outperform the phrase-based system on an out-of-domain test set,
making it attractive for real-world deployment.
