Gender Aware Spoken Language Translation Applied to English-Arabic

Spoken Language Translation (SLT) is becoming more widely used and becoming a communication tool that helps in crossing language barriers. One of the challenges of SLT is the translation from a language without gender agreement to a language with gender agreement such as English to Arabic. In this paper, we introduce an approach to tackle such limitation by enabling a Neural Machine Translation system to produce gender-aware unbiased translation. We show that NMT system can model the speaker/listener gender information to produce gender-aware translation that reduces the bias effect resulting from having training data dominated by particular gender forms. We propose a method to generate data used in adapting a NMT system to produce gender-aware and unbiased translation. The proposed approach can achieve significant improvement of the translation quality by 2 BLEU points.