@inproceedings{sheppard-etal-2024-biasly,
title = "Biasly: An Expert-Annotated Dataset for Subtle Misogyny Detection and Mitigation",
author = "Sheppard, Brooklyn and
Richter, Anna and
Cohen, Allison and
Smith, Elizabeth and
Kneese, Tamara and
Pelletier, Carolyne and
Baldini, Ioana and
Dong, Yue",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.24",
pages = "427--452",
abstract = "Using novel approaches to dataset development, the Biasly dataset captures the nuance and subtlety of misogyny in ways that are unique within the literature. Built in collaboration with multi-disciplinary experts and annotators themselves, the dataset contains annotations of movie subtitles, capturing colloquial expressions of misogyny in North American film. The open-source dataset can be used for a range of NLP tasks, including binary and multi-label classification, severity score regression, and text generation for rewrites. In this paper, we discuss the methodology used, analyze the annotations obtained, provide baselines for each task using common NLP algorithms, and furnish error analyses to give insight into model behaviour when fine-tuned on the Biasly dataset.",
}
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%0 Conference Proceedings
%T Biasly: An Expert-Annotated Dataset for Subtle Misogyny Detection and Mitigation
%A Sheppard, Brooklyn
%A Richter, Anna
%A Cohen, Allison
%A Smith, Elizabeth
%A Kneese, Tamara
%A Pelletier, Carolyne
%A Baldini, Ioana
%A Dong, Yue
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F sheppard-etal-2024-biasly
%X Using novel approaches to dataset development, the Biasly dataset captures the nuance and subtlety of misogyny in ways that are unique within the literature. Built in collaboration with multi-disciplinary experts and annotators themselves, the dataset contains annotations of movie subtitles, capturing colloquial expressions of misogyny in North American film. The open-source dataset can be used for a range of NLP tasks, including binary and multi-label classification, severity score regression, and text generation for rewrites. In this paper, we discuss the methodology used, analyze the annotations obtained, provide baselines for each task using common NLP algorithms, and furnish error analyses to give insight into model behaviour when fine-tuned on the Biasly dataset.
%U https://aclanthology.org/2024.findings-acl.24
%P 427-452
Markdown (Informal)
[Biasly: An Expert-Annotated Dataset for Subtle Misogyny Detection and Mitigation](https://aclanthology.org/2024.findings-acl.24) (Sheppard et al., Findings 2024)
ACL
- Brooklyn Sheppard, Anna Richter, Allison Cohen, Elizabeth Smith, Tamara Kneese, Carolyne Pelletier, Ioana Baldini, and Yue Dong. 2024. Biasly: An Expert-Annotated Dataset for Subtle Misogyny Detection and Mitigation. In Findings of the Association for Computational Linguistics ACL 2024, pages 427–452, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.