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.
A. Almahairi et al. REF , proposed NMT for the task of Arabic translation in both directions (Arabic-English and English-Arabic) and compared a Vanilla Attention-based NMT system against a Vanilla Phrase-based system.
First Result on Arabic Neural Machine Translation
ArXiv
ArXiv
Computer Science