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AbstractThe Explainable Detection of Online Sexism task presents the problem of explainable sexism detection through fine-grained categorisation of sexist cases with three subtasks. Our team experimented with different ways to …...

AbstractSexism is a harmful phenomenon that provokes gender inequalities and social imbalances. The expanding application of sexist content on social media platforms creates an unwelcoming and discomforting environment for many …...

AbstractThis paper presents the submissions of the DH-FBK team for the three tasks of Task 10 at SemEval 2023. The Explainable Detection of Online Sexism (EDOS) task aims at detecting …...

AbstractUsing pre-trained language models to implement classifiers from small to modest amounts of training data is an area of active research. The ability of large language models to generalize from …...

AbstractThis paper presents our work on the SemEval-2023 Task 10 Explainable Detection of Online Sexism (EDOS) using lexicon-based models. Our approach consists of three main steps: lexicon construction based on …...

AbstractOnline sexism is a widespread and harmful phenomenon. Automated tools can assist the detection of sexism at scale. Binary detection, however, disregards the diversity of sexist content, and fails to …...

AbstractThis 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 …...

AbstractThere is no simple definition of sexism, butit can be described as prejudice, stereotyping,or discrimination, especially against women,based on their gender. In online interactions,sexism is common. One out of ten …...

AbstractWe present our submission to SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS). We address all three tasks: Task A consists of identifying whether a post is sexist. If …...