Unsupervised Discovery of Gendered Language through Latent-Variable Modeling

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Unsupervised Discovery of Gendered Language through Latent-Variable Modeling. / Hoyle, Alexander Miserlis; Wolf-sonkin, Lawrence; Wallach, Hanna; Augenstein, Isabelle; Cotterell, Ryan.

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2020. p. 1706-1716.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Hoyle, AM, Wolf-sonkin, L, Wallach, H, Augenstein, I & Cotterell, R 2020, Unsupervised Discovery of Gendered Language through Latent-Variable Modeling. in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, pp. 1706-1716, 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, 01/07/2019. https://doi.org/10.18653/v1/P19-1167

APA

Hoyle, A. M., Wolf-sonkin, L., Wallach, H., Augenstein, I., & Cotterell, R. (2020). Unsupervised Discovery of Gendered Language through Latent-Variable Modeling. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 1706-1716). Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1167

Vancouver

Hoyle AM, Wolf-sonkin L, Wallach H, Augenstein I, Cotterell R. Unsupervised Discovery of Gendered Language through Latent-Variable Modeling. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics. 2020. p. 1706-1716 https://doi.org/10.18653/v1/P19-1167

Author

Hoyle, Alexander Miserlis ; Wolf-sonkin, Lawrence ; Wallach, Hanna ; Augenstein, Isabelle ; Cotterell, Ryan. / Unsupervised Discovery of Gendered Language through Latent-Variable Modeling. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2020. pp. 1706-1716

Bibtex

@inproceedings{381fda88a4ec49358483691a8fb47df4,
title = "Unsupervised Discovery of Gendered Language through Latent-Variable Modeling",
abstract = "Studying the ways in which language is gendered has long been an area of interest in sociolinguistics. Studies have explored, for example, the speech of male and female characters in film and the language used to describe male and female politicians. In this paper, we aim not to merely study this phenomenon qualitatively, but instead to quantify the degree to which the language used to describe men and women is different and, moreover, different in a positive or negative way. To that end, we introduce a generative latent-variable model that jointly represents adjective (or verb) choice, with its sentiment, given the natural gender of a head (or dependent) noun. We find that there are significant differences between descriptions of male and female nouns and that these differences align with common gender stereotypes: Positive adjectives used to describe women are more often related to their bodies than adjectives used to describe men",
author = "Hoyle, {Alexander Miserlis} and Lawrence Wolf-sonkin and Hanna Wallach and Isabelle Augenstein and Ryan Cotterell",
year = "2020",
doi = "10.18653/v1/P19-1167",
language = "English",
pages = "1706--1716",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics",
note = "57th Annual Meeting of the Association for Computational Linguistics ; Conference date: 01-07-2019 Through 01-07-2019",

}

RIS

TY - GEN

T1 - Unsupervised Discovery of Gendered Language through Latent-Variable Modeling

AU - Hoyle, Alexander Miserlis

AU - Wolf-sonkin, Lawrence

AU - Wallach, Hanna

AU - Augenstein, Isabelle

AU - Cotterell, Ryan

PY - 2020

Y1 - 2020

N2 - Studying the ways in which language is gendered has long been an area of interest in sociolinguistics. Studies have explored, for example, the speech of male and female characters in film and the language used to describe male and female politicians. In this paper, we aim not to merely study this phenomenon qualitatively, but instead to quantify the degree to which the language used to describe men and women is different and, moreover, different in a positive or negative way. To that end, we introduce a generative latent-variable model that jointly represents adjective (or verb) choice, with its sentiment, given the natural gender of a head (or dependent) noun. We find that there are significant differences between descriptions of male and female nouns and that these differences align with common gender stereotypes: Positive adjectives used to describe women are more often related to their bodies than adjectives used to describe men

AB - Studying the ways in which language is gendered has long been an area of interest in sociolinguistics. Studies have explored, for example, the speech of male and female characters in film and the language used to describe male and female politicians. In this paper, we aim not to merely study this phenomenon qualitatively, but instead to quantify the degree to which the language used to describe men and women is different and, moreover, different in a positive or negative way. To that end, we introduce a generative latent-variable model that jointly represents adjective (or verb) choice, with its sentiment, given the natural gender of a head (or dependent) noun. We find that there are significant differences between descriptions of male and female nouns and that these differences align with common gender stereotypes: Positive adjectives used to describe women are more often related to their bodies than adjectives used to describe men

U2 - 10.18653/v1/P19-1167

DO - 10.18653/v1/P19-1167

M3 - Article in proceedings

SP - 1706

EP - 1716

BT - Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

PB - Association for Computational Linguistics

T2 - 57th Annual Meeting of the Association for Computational Linguistics

Y2 - 1 July 2019 through 1 July 2019

ER -

ID: 240629975