K. Chernyshev, E. Garanina, Duygu Bayram, Qiankun Zheng, Lukas Edman
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引用次数: 0
Abstract
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. 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.
厌女症和性别歧视是社交媒体上日益严重的问题。在线性别歧视检测已经取得了进步,但这些系统往往是无法解释的。semevale -2023 Task 10 on Explainable Detection of Online Sexism旨在提高性别歧视检测的可解释性,我们的团队参与了所有提议的子任务。我们的系统是基于进一步的领域自适应预训练。基于具有域适应性的基于transformer的模型,我们将微调与多任务学习进行比较,并显示每个子任务需要不同的系统配置。在我们的实验中,多任务学习在性别歧视检测方面的表现与标准微调相当,在粗粒度性别歧视分类方面明显更好,而在细粒度分类方面,微调更可取。