@inproceedings{mahmoudi-2023-iust,
    title = "{IUST}{\_}{NLP} at {S}em{E}val-2023 Task 10: Explainable Detecting Sexism with Transformers and Task-adaptive Pretraining",
    author = "Mahmoudi, Hadiseh",
    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-1.69",
    doi = "10.18653/v1/2023.semeval-1.69",
    pages = "498--505",
    abstract = "This paper describes our system on SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS). This work aims to design an automatic system for detecting and classifying sexist content in online spaces. We propose a set of transformer-based pre-trained models with task-adaptive pretraining and ensemble learning. The main contributions of our system include analyzing the performance of different transformer-based pre-trained models and combining these models, as well as providing an efficient method using large amounts of unlabeled data for model adaptive pretraining. We have also explored several other strategies. On the test dataset, our system achieves F1-scores of 83{\%}, 64{\%}, and 47{\%} on subtasks A, B, and C, respectively.",
}

This paper describes our system on SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS). This work aims to design an automatic system for detecting and classifying sexist content in online spaces. We propose a set of transformer-based pre-trained models with task-adaptive pretraining and ensemble learning. The main contributions of our system include analyzing the performance of different transformer-based pre-trained models and combining these models, as well as providing an efficient method using large amounts of unlabeled data for model adaptive pretraining. We have also explored several other strategies. On the test dataset, our system achieves F1-scores of 83{\%}, 64{\%}, and 47{\%} on subtasks A, B, and C, respectively.
Mahmoudi, Hadiseh
IUST_NLP at SemEval-2023 Task 10: Explainable Detecting Sexism with Transformers and Task-adaptive Pretraining