SemEval-2023 Task 10: Explainable Detection of Online Sexism

Hannah Rose Kirk, Wenjie Yin, Bertie Vidgen, Paul Röttger
3/7/2023
cs.CLcs.CY

No official code found. Best implementation: rewire-online/edos (27 stars, CC0-1.0, last commit 2023-04-13).

Abstract

Online sexism is a widespread and harmful phenomenon. Automated tools can assist the detection of sexism at scale. Binary detection, however, disregards the diversity of sexist content, and fails to provide clear explanations for why something is sexist. To address this issue, we introduce SemEval Task 10 on the Explainable Detection of Online Sexism (EDOS). We make three main contributions: i) a novel hierarchical taxonomy of sexist content, which includes granular vectors of sexism to aid explainability; ii) a new dataset of 20,000 social media comments with fine-grained labels, along with larger unlabelled datasets for model adaptation; and iii) baseline models as well as an analysis of the methods, results and errors for participant submissions to our task.

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rewire-online/edosCommunity100%
CC0-1.0

Public repository for SemEval 2023 - Task 10 - Explainable Detection of Online Sexism (EDOS)

277PythonAug 27, 20223 years agoCC0-1.0

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Cite this paper

@article{kirk2023semeval,
  title  = {SemEval-2023 Task 10: Explainable Detection of Online Sexism},
  author = {Hannah Rose Kirk and Wenjie Yin and Bertie Vidgen and Paul Röttger},
  year   = {2023},
  eprint = {2303.04222},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.04222v2}
}

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