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-可解释检测在线性别歧视(EDOS)中的提交情况,分为三个子任务。社交媒体平台的兴起导致女性在社交媒体平台上遭受不成比例的性别歧视现象增加。这使得检测和解释在线性别歧视内容比以往任何时候都更加重要,以使社交媒体对女性更安全、更易访问。我们的方法包括对BERT模型进行试验和微调,并使用多数表决集成模型,优于单个基准模型得分。我们的系统在任务A、B和C上分别实现了宏F1分数0.8392、0.6092和0.4319。