This paper describes our submission to Task 10 at SemEval 2023-Explainable Detection of Online Sexism (EDOS), divided into three subtasks. The recent rise in social media platforms has seen an increase in disproportionate levels of sexism experienced by women on social media platforms. This has made detecting and explaining online sexist content more important than ever to make social media safer and more accessible for women. Our approach consists of experimenting and finetuning BERT-based models and using a Majority Voting ensemble model that outperforms individual baseline model scores. Our system achieves a macro F1 score of 0.8392 for Task A, 0.6092 for Task B, and 0.4319 for Task C.

翻译：本文描述了我们在SemEval 2023任务10——可解释性网络性别歧视检测（EDOS）中的参赛方案，该任务分为三个子任务。近年来社交媒体平台的兴起导致女性在网络平台上遭受不成比例程度的性别歧视现象日益增加。这使得检测和解释网络性别歧视内容比以往任何时候都更为重要，旨在为女性创造更安全、更易获取的社交媒体环境。我们的方法包括对基于BERT的模型进行实验与微调，并采用多数投票集成模型，该模型性能优于各基线模型的单模型得分。我们的系统在任务A上取得了0.8392的宏F1分数，任务B为0.6092，任务C为0.4319。