@inproceedings{tavan-najafi-2023-marsan,
    title = "{M}ar{S}an at {S}em{E}val-2023 Task 10: Can Adversarial Training with help of a Graph Convolutional Network Detect Explainable Sexism?",
    author = "Tavan, Ehsan  and
      Najafi, Maryam",
    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-1.139",
    doi = "10.18653/v1/2023.semeval-1.139",
    pages = "1011--1020",
    abstract = "This paper describes SemEval-2022{'}s shared task {``}Explainable Detection of Online Sexism{''}. The fine-grained classification of sexist content plays a major role in building explainable frameworks for online sexism detection. We hypothesize that by encoding dependency information using Graph Convolutional Networks (GCNs) we may capture more stylistic information about sexist contents. Online sexism has the potential to cause significant harm to women who are the targets of such behavior. It not only creates unwelcoming and inaccessible spaces for women online but also perpetuates social asymmetries and injustices. We believed improving the robustness and generalization ability of neural networks during training will allow models to capture different belief distributions for sexism categories. So we proposed adversarial training with GCNs for explainable detection of online sexism. In the end, our proposed method achieved very competitive results in all subtasks and shows that adversarial training of GCNs is a promising method for the explainable detection of online sexism.",
}

This paper describes SemEval-2022{'}s shared task {``}Explainable Detection of Online Sexism{''}. The fine-grained classification of sexist content plays a major role in building explainable frameworks for online sexism detection. We hypothesize that by encoding dependency information using Graph Convolutional Networks (GCNs) we may capture more stylistic information about sexist contents. Online sexism has the potential to cause significant harm to women who are the targets of such behavior. It not only creates unwelcoming and inaccessible spaces for women online but also perpetuates social asymmetries and injustices. We believed improving the robustness and generalization ability of neural networks during training will allow models to capture different belief distributions for sexism categories. So we proposed adversarial training with GCNs for explainable detection of online sexism. In the end, our proposed method achieved very competitive results in all subtasks and shows that adversarial training of GCNs is a promising method for the explainable detection of online sexism.
Tavan, Ehsan Najafi, Maryam
MarSan at SemEval-2023 Task 10: Can Adversarial Training with help of a Graph Convolutional Network Detect Explainable Sexism?