@inproceedings{younes-etal-2023-alexa,
    title = "{A}lexa at {S}em{E}val-2023 Task 10: Ensemble Modeling of {D}e{BERT}a and {BERT} Variations for Identifying Sexist Text",
    author = "Younes, Mutaz  and
      Kharabsheh, Ali  and
      Bani Younes, Mohammad",
    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/add-emnlp-2024-awards/2023.semeval-1.228/",
    doi = "10.18653/v1/2023.semeval-1.228",
    pages = "1644--1649",
    abstract = "This study presents an ensemble approach for detecting sexist text in the context of the Semeval-2023 task 10. Our approach leverages 18 models, including DeBERTa-v3-base models with different input sequence lengths, a BERT-based model trained on identifying hate speech, and three more models pre-trained on the task`s unlabeled data with varying input lengths. The results of our framework on the development set show an f1-score of 84.92{\%} and on the testing set 84.55{\%}, effectively demonstrating the strength of the ensemble approach in getting accurate results."
}