First Result on Arabic Neural Machine Translation

Abstract

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.

EMNLP submission

References in corpus (4)

• Sequence to Sequence Learning with Neural Networks
• ADADELTA: An Adaptive Learning Rate Method
• On Using Very Large Target Vocabulary for Neural Machine Translation
• Natural Language Understanding with Distributed Representation

Cited by in corpus (7)

• The Impact of Preprocessing on Arabic-English Statistical and Neural Machine Translation
• Machine-Translation History and Evolution: Survey for Arabic-English Translations
• A Recipe for Arabic-English Neural Machine Translation
• Large-Scale Machine Translation between Arabic and Hebrew: Available Corpora and Initial Results
• Low Resourced Machine Translation via Morpho-syntactic Modeling: The Case of Dialectal Arabic
• Multi-Task Learning for Cross-Lingual Abstractive Summarization
• Amharic-Arabic Neural Machine Translation