CL-UZH at SemEval-2023 Task 10: Sexism Detection through Incremental
  Fine-Tuning and Multi-Task Learning with Label Descriptions
Janis Goldzycher
The widespread popularity of social media has led to an increase in hateful,
abusive, and sexist language, motivating methods for the automatic detection of
such phenomena. The goal of the SemEval shared task \textit{Towards Explainable
Detection of Online Sexism} (EDOS 2023) is to detect sexism in English social
media posts (subtask A), and to categorize such posts into four coarse-grained
sexism categories (subtask B), and eleven fine-grained subcategories (subtask
C). 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 related
tasks and datasets before being fine-tuned on the specific EDOS subtasks. We
implement multi-task learning by formulating each task as binary pairwise text
classification, where the dataset and label descriptions are given along with
the input text. The results show clear improvements over a fine-tuned
DeBERTa-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).