This paper describes our system on SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS). This work aims to design an automatic system for detecting and classifying sexist content in online spaces. We propose a set of transformer-based pre-trained models with task-adaptive pretraining and ensemble learning. The main contributions of our system include analyzing the performance of different transformer-based pre-trained models and combining these models, as well as providing an efficient method using large amounts of unlabeled data for model adaptive pretraining. We have also explored several other strategies. On the test dataset, our system achieves F1-scores of 83%, 64%, and 47% on subtasks A, B, and C, respectively.

翻译：本文描述了我们在SemEval-2023任务十：可解释性在线性别歧视检测（EDOS）中的系统。该工作旨在设计一个自动检测和分类网络空间中性别歧视内容的系统。我们提出了一套基于Transformer的预训练模型，结合了任务自适应预训练和集成学习。本系统的主要贡献包括：分析不同基于Transformer的预训练模型的性能并整合这些模型，以及提供一种利用大量未标注数据进行模型自适应预训练的高效方法。我们还探索了其他若干策略。在测试数据集上，我们的系统在子任务A、B和C上分别取得了83%、64%和47%的F1分数。