@inproceedings{matthews-etal-2021-gender,
title = "Gender Bias in Natural Language Processing Across Human Languages",
author = "Matthews, Abigail and
Grasso, Isabella and
Mahoney, Christopher and
Chen, Yan and
Wali, Esma and
Middleton, Thomas and
Njie, Mariama and
Matthews, Jeanna",
editor = "Pruksachatkun, Yada and
Ramakrishna, Anil and
Chang, Kai-Wei and
Krishna, Satyapriya and
Dhamala, Jwala and
Guha, Tanaya and
Ren, Xiang",
booktitle = "Proceedings of the First Workshop on Trustworthy Natural Language Processing",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.trustnlp-1.6",
doi = "10.18653/v1/2021.trustnlp-1.6",
pages = "45--54",
abstract = "Natural Language Processing (NLP) systems are at the heart of many critical automated decision-making systems making crucial recommendations about our future world. Gender bias in NLP has been well studied in English, but has been less studied in other languages. In this paper, a team including speakers of 9 languages - Chinese, Spanish, English, Arabic, German, French, Farsi, Urdu, and Wolof - reports and analyzes measurements of gender bias in the Wikipedia corpora for these 9 languages. We develop extensions to profession-level and corpus-level gender bias metric calculations originally designed for English and apply them to 8 other languages, including languages that have grammatically gendered nouns including different feminine, masculine, and neuter profession words. We discuss future work that would benefit immensely from a computational linguistics perspective.",
}
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<abstract>Natural Language Processing (NLP) systems are at the heart of many critical automated decision-making systems making crucial recommendations about our future world. Gender bias in NLP has been well studied in English, but has been less studied in other languages. In this paper, a team including speakers of 9 languages - Chinese, Spanish, English, Arabic, German, French, Farsi, Urdu, and Wolof - reports and analyzes measurements of gender bias in the Wikipedia corpora for these 9 languages. We develop extensions to profession-level and corpus-level gender bias metric calculations originally designed for English and apply them to 8 other languages, including languages that have grammatically gendered nouns including different feminine, masculine, and neuter profession words. We discuss future work that would benefit immensely from a computational linguistics perspective.</abstract>
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%0 Conference Proceedings
%T Gender Bias in Natural Language Processing Across Human Languages
%A Matthews, Abigail
%A Grasso, Isabella
%A Mahoney, Christopher
%A Chen, Yan
%A Wali, Esma
%A Middleton, Thomas
%A Njie, Mariama
%A Matthews, Jeanna
%Y Pruksachatkun, Yada
%Y Ramakrishna, Anil
%Y Chang, Kai-Wei
%Y Krishna, Satyapriya
%Y Dhamala, Jwala
%Y Guha, Tanaya
%Y Ren, Xiang
%S Proceedings of the First Workshop on Trustworthy Natural Language Processing
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F matthews-etal-2021-gender
%X Natural Language Processing (NLP) systems are at the heart of many critical automated decision-making systems making crucial recommendations about our future world. Gender bias in NLP has been well studied in English, but has been less studied in other languages. In this paper, a team including speakers of 9 languages - Chinese, Spanish, English, Arabic, German, French, Farsi, Urdu, and Wolof - reports and analyzes measurements of gender bias in the Wikipedia corpora for these 9 languages. We develop extensions to profession-level and corpus-level gender bias metric calculations originally designed for English and apply them to 8 other languages, including languages that have grammatically gendered nouns including different feminine, masculine, and neuter profession words. We discuss future work that would benefit immensely from a computational linguistics perspective.
%R 10.18653/v1/2021.trustnlp-1.6
%U https://aclanthology.org/2021.trustnlp-1.6
%U https://doi.org/10.18653/v1/2021.trustnlp-1.6
%P 45-54
Markdown (Informal)
[Gender Bias in Natural Language Processing Across Human Languages](https://aclanthology.org/2021.trustnlp-1.6) (Matthews et al., TrustNLP 2021)
ACL
- Abigail Matthews, Isabella Grasso, Christopher Mahoney, Yan Chen, Esma Wali, Thomas Middleton, Mariama Njie, and Jeanna Matthews. 2021. Gender Bias in Natural Language Processing Across Human Languages. In Proceedings of the First Workshop on Trustworthy Natural Language Processing, pages 45–54, Online. Association for Computational Linguistics.