@inproceedings{rifat-etal-2023-acsmkrhr,
    title = "{ACSMKRHR} at {S}em{E}val-2023 Task 10: Explainable Online Sexism Detection({EDOS})",
    author = "Rifat, Rakib Hossain  and
      Shruti, Abanti  and
      Kamal, Marufa  and
      Sadeque, Farig",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.99",
    doi = "10.18653/v1/2023.semeval-1.99",
    pages = "724--732",
    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.",
}

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.
Rifat, Rakib Hossain Shruti, Abanti Kamal, Marufa Sadeque, Farig
ACSMKRHR at SemEval-2023 Task 10: Explainable Online Sexism Detection(EDOS)