Abstract
Gender bias in machine translation can manifest when choosing gender inflections based on spurious gender correlations. For example, always translating doctors as men and nurses as women. This can be particularly harmful as models become more popular and deployed within commercial systems. Our work presents the largest evidence for the phenomenon in more than 19 systems submitted to the WMT over four diverse target languages: Czech, German, Polish, and Russian. To achieve this, we use WinoMT, a recent automatic test suite which examines gender coreference and bias when translating from English to languages with grammatical gender. We extend WinoMT to handle two new languages tested in WMT: Polish and Czech. We find that all systems consistently use spurious correlations in the data rather than meaningful contextual information.- Anthology ID:
- 2020.wmt-1.39
- Volume:
- Proceedings of the Fifth Conference on Machine Translation
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Yvette Graham, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 357–364
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.39
- DOI:
- Bibkey:
- Cite (ACL):
- Tom Kocmi, Tomasz Limisiewicz, and Gabriel Stanovsky. 2020. Gender Coreference and Bias Evaluation at WMT 2020. In Proceedings of the Fifth Conference on Machine Translation, pages 357–364, Online. Association for Computational Linguistics.
- Cite (Informal):
- Gender Coreference and Bias Evaluation at WMT 2020 (Kocmi et al., WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.39.pdf
- Video:
- https://slideslive.com/38939659
- Data
- WMT 2020
Export citation
@inproceedings{kocmi-etal-2020-gender,
title = "Gender Coreference and Bias Evaluation at {WMT} 2020",
author = "Kocmi, Tom and
Limisiewicz, Tomasz and
Stanovsky, Gabriel",
editor = {Barrault, Lo{\"\i}c and
Bojar, Ond{\v{r}}ej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss{\`a}, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Graham, Yvette and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr{\'e} and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.39",
pages = "357--364",
abstract = "Gender bias in machine translation can manifest when choosing gender inflections based on spurious gender correlations. For example, always translating doctors as men and nurses as women. This can be particularly harmful as models become more popular and deployed within commercial systems. Our work presents the largest evidence for the phenomenon in more than 19 systems submitted to the WMT over four diverse target languages: Czech, German, Polish, and Russian. To achieve this, we use WinoMT, a recent automatic test suite which examines gender coreference and bias when translating from English to languages with grammatical gender. We extend WinoMT to handle two new languages tested in WMT: Polish and Czech. We find that all systems consistently use spurious correlations in the data rather than meaningful contextual information.",
}
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%0 Conference Proceedings %T Gender Coreference and Bias Evaluation at WMT 2020 %A Kocmi, Tom %A Limisiewicz, Tomasz %A Stanovsky, Gabriel %Y Barrault, Loïc %Y Bojar, Ondřej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussà, Marta R. %Y Federmann, Christian %Y Fishel, Mark %Y Fraser, Alexander %Y Graham, Yvette %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Martins, André %Y Morishita, Makoto %Y Monz, Christof %Y Nagata, Masaaki %Y Nakazawa, Toshiaki %Y Negri, Matteo %S Proceedings of the Fifth Conference on Machine Translation %D 2020 %8 November %I Association for Computational Linguistics %C Online %F kocmi-etal-2020-gender %X Gender bias in machine translation can manifest when choosing gender inflections based on spurious gender correlations. For example, always translating doctors as men and nurses as women. This can be particularly harmful as models become more popular and deployed within commercial systems. Our work presents the largest evidence for the phenomenon in more than 19 systems submitted to the WMT over four diverse target languages: Czech, German, Polish, and Russian. To achieve this, we use WinoMT, a recent automatic test suite which examines gender coreference and bias when translating from English to languages with grammatical gender. We extend WinoMT to handle two new languages tested in WMT: Polish and Czech. We find that all systems consistently use spurious correlations in the data rather than meaningful contextual information. %U https://aclanthology.org/2020.wmt-1.39 %P 357-364
Markdown (Informal)
[Gender Coreference and Bias Evaluation at WMT 2020](https://aclanthology.org/2020.wmt-1.39) (Kocmi et al., WMT 2020)
- Gender Coreference and Bias Evaluation at WMT 2020 (Kocmi et al., WMT 2020)
ACL
- Tom Kocmi, Tomasz Limisiewicz, and Gabriel Stanovsky. 2020. Gender Coreference and Bias Evaluation at WMT 2020. In Proceedings of the Fifth Conference on Machine Translation, pages 357–364, Online. Association for Computational Linguistics.