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.

翻译：在线性别歧视是一种普遍且有害的现象。自动化工具能够辅助大规模检测性别歧视内容。然而，二元检测忽略了性别歧视内容的多样性，且无法为“某内容为何具有性别歧视性质”提供清晰解释。为解决这一问题，我们提出SemEval任务10——在线性别歧视的可解释性检测（EDOS）。我们的主要贡献包括：i）提出一种新颖的分层性别歧视内容分类体系，其中包含细粒度性别歧视向量以增强可解释性；ii）构建包含20,000条社交媒体评论及细粒度标签的新数据集，同时提供更大的无标签数据集用于模型适配；iii）提供基线模型，并对参与者的提交方法、结果及错误进行系统性分析。