@inproceedings{kondragunta-etal-2023-skam,
    title = "{SKAM} at {S}em{E}val-2023 Task 10: Linguistic Feature Integration and Continuous Pretraining for Online Sexism Detection and Classification",
    author = "Kondragunta, Murali Manohar  and
      Chen, Amber  and
      Slot, Karlo  and
      Weering, Sanne  and
      Caselli, Tommaso",
    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-1.250",
    doi = "10.18653/v1/2023.semeval-1.250",
    pages = "1805--1817",
    abstract = "Sexism has been prevalent online. In this paper, we explored the effect of explicit linguistic features and continuous pretraining on the performance of pretrained language models in sexism detection. While adding linguistic features did not improve the performance of the model, continuous pretraining did slightly boost the performance of the model in Task B from a mean macro-F1 score of 0.6156 to 0.6246. The best mean macro-F1 score in Task A was achieved by a finetuned HateBERT model using regular pretraining (0.8331). We observed that the linguistic features did not improve the model{'}s performance. At the same time, continuous pretraining proved beneficial only for nuanced downstream tasks like Task-B.",
}

Sexism has been prevalent online. In this paper, we explored the effect of explicit linguistic features and continuous pretraining on the performance of pretrained language models in sexism detection. While adding linguistic features did not improve the performance of the model, continuous pretraining did slightly boost the performance of the model in Task B from a mean macro-F1 score of 0.6156 to 0.6246. The best mean macro-F1 score in Task A was achieved by a finetuned HateBERT model using regular pretraining (0.8331). We observed that the linguistic features did not improve the model{'}s performance. At the same time, continuous pretraining proved beneficial only for nuanced downstream tasks like Task-B.
Kondragunta, Murali Manohar Chen, Amber Slot, Karlo Weering, Sanne Caselli, Tommaso
SKAM at SemEval-2023 Task 10: Linguistic Feature Integration and Continuous Pretraining for Online Sexism Detection and Classification