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The widespread popularity of social media has led to an increase in hateful,abusive, and sexist language, motivating methods for the automatic detection ofsuch phenomena. The goal of the SemEval shared task \textit{Towards ExplainableDetection of Online Sexism} (EDOS 2023) is to detect sexism in English socialmedia posts (subtask A), and to categorize such posts into four coarse-grainedsexism categories (subtask B), and eleven fine-grained subcategories (subtaskC). In this paper, we present our submitted systems for all three subtasks,based on a multi-task model that has been fine-tuned on a range of relatedtasks and datasets before being fine-tuned on the specific EDOS subtasks. Weimplement multi-task learning by formulating each task as binary pairwise textclassification, where the dataset and label descriptions are given along withthe input text. The results show clear improvements over a fine-tunedDeBERTa-V3 serving as a baseline leading to $F_1$-scores of 85.9\% in subtask A(rank 13/84), 64.8\% in subtask B (rank 19/69), and 44.9\% in subtask C(26/63).

CL-UZH at SemEval-2023 Task 10: Sexism Detection through Incremental Fine-Tuning and Multi-Task Learning with Label Descriptions