Gender Aware Spoken Language Translation Applied to English-Arabic

TL;DR

This paper presents a neural machine translation approach that incorporates speaker and listener gender information to improve English-Arabic spoken language translation, addressing gender agreement challenges.

Contribution

It introduces a novel method to generate data for adapting NMT systems to produce gender-aware translations in SLT.

Findings

Achieved a 2 BLEU point improvement in translation quality.

Demonstrated modeling of speaker/listener gender information enhances translation.

Proposed data generation method effectively adapts NMT for gender awareness.

Abstract

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. We propose a method to generate data used in adapting a NMT system to produce gender-aware. The proposed approach can achieve significant improvement of the translation quality by 2 BLEU points.