Abstract
People are expressing their opinions online for a lot of years now. Although these opinions and comments provide people an opportunity of expressing their views, there is a lot of hate speech that can be found online. More specifically, sexist comments are very popular affecting and creating a negative impact on a lot of women and girls online. This paper describes the approaches of the SemEval-2023 Task 10 competition for Explainable Online Sexism Detection (EDOS). The task has been divided into 3 subtasks, introducing different classes of sexist comments. We have approached these tasks using the bert-cased and uncased models which are trained on the annotated dataset that has been provided in the competition. Task A provided the best F1 score of 80% on the test set, and tasks B and C provided 58% and 40% respectively.
Cite
CITATION STYLE
Rifat, R. H., Shruti, A. C., Kamal, M., & Sadeque, F. (2023). ACSMKRHR at SemEval-2023 Task 10: Explainable Online Sexism Detection(EDOS). In 17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop (pp. 724–732). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.semeval-1.99
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