@inproceedings{hovy-etal-2020-sound,
title = "{\textquotedblleft}You Sound Just Like Your Father{\textquotedblright} Commercial Machine Translation Systems Include Stylistic Biases",
author = "Hovy, Dirk and
Bianchi, Federico and
Fornaciari, Tommaso",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.154/",
doi = "10.18653/v1/2020.acl-main.154",
pages = "1686--1690",
abstract = "The main goal of machine translation has been to convey the correct content. Stylistic considerations have been at best secondary. We show that as a consequence, the output of three commercial machine translation systems (Bing, DeepL, Google) make demographically diverse samples from five languages {\textquotedblleft}sound{\textquotedblright} older and more male than the original. Our findings suggest that translation models reflect demographic bias in the training data. This opens up interesting new research avenues in machine translation to take stylistic considerations into account."
}
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%0 Conference Proceedings
%T “You Sound Just Like Your Father” Commercial Machine Translation Systems Include Stylistic Biases
%A Hovy, Dirk
%A Bianchi, Federico
%A Fornaciari, Tommaso
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F hovy-etal-2020-sound
%X The main goal of machine translation has been to convey the correct content. Stylistic considerations have been at best secondary. We show that as a consequence, the output of three commercial machine translation systems (Bing, DeepL, Google) make demographically diverse samples from five languages “sound” older and more male than the original. Our findings suggest that translation models reflect demographic bias in the training data. This opens up interesting new research avenues in machine translation to take stylistic considerations into account.
%R 10.18653/v1/2020.acl-main.154
%U https://aclanthology.org/2020.acl-main.154/
%U https://doi.org/10.18653/v1/2020.acl-main.154
%P 1686-1690
Markdown (Informal)
[“You Sound Just Like Your Father” Commercial Machine Translation Systems Include Stylistic Biases](https://aclanthology.org/2020.acl-main.154/) (Hovy et al., ACL 2020)
ACL