Machine translation (MT) technology has facilitated our daily tasks by providing accessible shortcuts for gathering, elaborating and communicating information. However, it can suffer from biases that harm users and society at large. As a relatively new field of inquiry, gender bias in MT still lacks internal cohesion, which advocates for a unified framework to ease future research. To this end, we: i) critically review current conceptualizations of bias in light of theoretical insights from related disciplines, ii) summarize previous analyses aimed at assessing gender bias in MT, iii) discuss the mitigating strategies proposed so far, and iv) point toward potential directions for future work.
@article{arxiv.2104.06001,
title = {Gender Bias in Machine Translation},
author = {Beatrice Savoldi and Marco Gaido and Luisa Bentivogli and Matteo Negri and Marco Turchi},
journal= {arXiv preprint arXiv:2104.06001},
year = {2021}
}
Comments
Accepted for publication in Transaction of the Association for Computational Linguistics (TACL), 2021