Misogyny and sexism are growing problems in social media. Advances have been made in online sexism detection but the systems are often uninterpretable. SemEval-2023 Task 10 on Explainable Detection of Online Sexism aims at increasing explainability of the sexism detection, and our team participated in all the proposed subtasks. Our system is based on further domain-adaptive pre-training (Gururangan et al., 2020). Building on the Transformer-based models with the domain adaptation, we compare fine-tuning with multi-task learning and show that each subtask requires a different system configuration. In our experiments, multi-task learning performs on par with standard fine-tuning for sexism detection and noticeably better for coarse-grained sexism classification, while fine-tuning is preferable for fine-grained classification.

翻译：在社交媒体中，厌女情绪与性别歧视问题日益严重。尽管在线性别歧视检测技术已取得进展，但相关系统往往缺乏可解释性。SemEval-2023 任务10"可解释在线性别歧视检测"旨在提升性别歧视检测的可解释性，本团队参与了该任务提出的所有子任务。我们的系统基于领域自适应预训练方法（Gururangan 等, 2020）。在采用领域自适应的 Transformer 模型基础上，我们比较了微调与多任务学习两种策略，结果表明每个子任务需要不同的系统配置。实验显示，在性别歧视检测任务中，多任务学习性能与标准微调相当；在粗粒度性别歧视分类中，多任务学习效果显著更优；而在细粒度分类任务中，微调方法更具优势。