@inproceedings{muti-etal-2023-uniboes,
    title = "{U}ni{B}oe{'}s at {S}em{E}val-2023 Task 10: Model-Agnostic Strategies for the Improvement of Hate-Tuned and Generative Models in the Classification of Sexist Posts",
    author = "Muti, Arianna  and
      Fernicola, Francesco  and
      Barr{\'o}n-Cede{\~n}o, Alberto",
    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-1.158",
    doi = "10.18653/v1/2023.semeval-1.158",
    pages = "1138--1147",
    abstract = "We present our submission to SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS). We address all three tasks: Task A consists of identifying whether a post is sexist. If so, Task B attempts to assign it one of four categories: threats, derogation, animosity, and prejudiced discussions. Task C aims for an even more fine-grained classification, divided among 11 classes. Our team UniBoe{'}s experiments with fine-tuning of hate-tuned Transformer-based models and priming for generative models. In addition, we explore model-agnostic strategies, such as data augmentation techniques combined with active learning, as well as obfuscation of identity terms. Our official submissions obtain an F1{\_}score of 0.83 for Task A, 0.58 for Task B and 0.32 for Task C.",
}

We present our submission to SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS). We address all three tasks: Task A consists of identifying whether a post is sexist. If so, Task B attempts to assign it one of four categories: threats, derogation, animosity, and prejudiced discussions. Task C aims for an even more fine-grained classification, divided among 11 classes. Our team UniBoe{'}s experiments with fine-tuning of hate-tuned Transformer-based models and priming for generative models. In addition, we explore model-agnostic strategies, such as data augmentation techniques combined with active learning, as well as obfuscation of identity terms. Our official submissions obtain an F1{\_}score of 0.83 for Task A, 0.58 for Task B and 0.32 for Task C.
Muti, Arianna Fernicola, Francesco Barr{\'o}n-Cede{\~n}o, Alberto
UniBoe's at SemEval-2023 Task 10: Model-Agnostic Strategies for the Improvement of Hate-Tuned and Generative Models in the Classification of Sexist Posts