@inproceedings{tasneem-etal-2023-kingsmantrio,
    title = "{K}ingsman{T}rio at {S}em{E}val-2023 Task 10: Analyzing the Effectiveness of Transfer Learning Models for Explainable Online Sexism Detection",
    author = "Tasneem, Fareen  and
      Hossain, Tashin  and
      Naim, Jannatun",
    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://preview.aclanthology.org/Ingest-2025-COMPUTEL/2023.semeval-1.263/",
    doi = "10.18653/v1/2023.semeval-1.263",
    pages = "1916--1920",
    abstract = "Online social platforms are now propagating sexist content endangering the involvement and inclusion of women on these platforms. Sexism refers to hostility, bigotry, or discrimination based on gender, typically against women. The proliferation of such notions deters women from engaging in social media spontaneously. Hence, detecting sexist content is critical to ensure a safe online platform where women can participate without the fear of being a target of sexism. This paper describes our participation in subtask A of SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS). This subtask requires classifying textual content as sexist or not sexist. We incorporate a RoBERTa-based architecture and further finetune the hyperparameters to entail better performance. The procured results depict the competitive performance of our approach among the other participants."
}