Gender Aware Spoken Language Translation Applied to English-Arabic

Understand

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 translation.
• We show that NMT system can model the speaker/listener gender information to produce gender-aware translation.