SemEval-2023 Task 10: Explainable Detection of Online Sexism

TL;DR

This paper introduces a new task, dataset, and baseline models for explainable detection of online sexism, aiming to improve understanding and transparency of sexist content in social media.

Contribution

It presents a hierarchical taxonomy of sexism, a large annotated dataset, and baseline models for explainable online sexism detection.

Findings

Hierarchical taxonomy enhances explainability

Dataset enables fine-grained analysis

Baseline models provide a starting point for future research

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.