@inproceedings{hassanawadalla2018gender,
author = {Hassan Awadalla, Hany and Elaraby, Mostafa and Tawfik, Ahmed and Khaled, Mahmoud and Osama, Aly},
title = {Gender Aware Spoken Language Translation Applied to English-Arabic},
booktitle = {2018 IEEE Proceedings of the Second International Conference on Natural Language and Speech Processing},
year = {2018},
month = {February},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/gender-aware-spoken-language-translation-applied-english-arabic/},
edition = {2018 IEEE Proceedings of the Second International Conference on Natural Language and Speech Processing},
}