@article{savoldi-etal-2021-gender,
title = "Gender Bias in Machine Translation",
author = "Savoldi, Beatrice and
Gaido, Marco and
Bentivogli, Luisa and
Negri, Matteo and
Turchi, Marco",
editor = "Roark, Brian and
Nenkova, Ani",
journal = "Transactions of the Association for Computational Linguistics",
volume = "9",
year = "2021",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/2021.tacl-1.51",
doi = "10.1162/tacl_a_00401",
pages = "845--874",
abstract = "AbstractMachine translation (MT) technology has facilitated our daily tasks by providing accessible shortcuts for gathering, processing, and communicating information. However, it can suffer from biases that harm users and society at large. As a relatively new field of inquiry, studies of gender bias in MT still lack cohesion. This 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.",
}
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%0 Journal Article
%T Gender Bias in Machine Translation
%A Savoldi, Beatrice
%A Gaido, Marco
%A Bentivogli, Luisa
%A Negri, Matteo
%A Turchi, Marco
%J Transactions of the Association for Computational Linguistics
%D 2021
%V 9
%I MIT Press
%C Cambridge, MA
%F savoldi-etal-2021-gender
%X AbstractMachine translation (MT) technology has facilitated our daily tasks by providing accessible shortcuts for gathering, processing, and communicating information. However, it can suffer from biases that harm users and society at large. As a relatively new field of inquiry, studies of gender bias in MT still lack cohesion. This 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.
%R 10.1162/tacl_a_00401
%U https://aclanthology.org/2021.tacl-1.51
%U https://doi.org/10.1162/tacl_a_00401
%P 845-874
Markdown (Informal)
[Gender Bias in Machine Translation](https://aclanthology.org/2021.tacl-1.51) (Savoldi et al., TACL 2021)
ACL
- Beatrice Savoldi, Marco Gaido, Luisa Bentivogli, Matteo Negri, and Marco Turchi. 2021. Gender Bias in Machine Translation. Transactions of the Association for Computational Linguistics, 9:845–874.