@inproceedings{aliyu-etal-2023-hausanlp,
    title = "{H}ausa{NLP} at {S}em{E}val-2023 Task 10: Transfer Learning, Synthetic Data and Side-information for Multi-level Sexism Classification",
    author = "Aliyu, Saminu Mohammad  and
      Abdulmumin, Idris  and
      Muhammad, Shamsuddeen Hassan  and
      Ahmad, Ibrahim Said  and
      Salahudeen, Saheed Abdullahi  and
      Yusuf, Aliyu  and
      Lawan, Falalu Ibrahim",
    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-1.273",
    doi = "10.18653/v1/2023.semeval-1.273",
    pages = "1983--1987",
    abstract = "We present the findings of our participation in the SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS) task, a shared task on offensive language (sexism) detection on English Gab and Reddit dataset. We investigated the effects of transferring two language models: XLM-T (sentiment classification) and HateBERT (same domain - Reddit) for multilevel classification into Sexist or not Sexist, and other subsequent sub-classifications of the sexist data. We also use synthetic classification of unlabelled dataset and intermediary class information to maximize the performance of our models. We submitted a system in Task A, and it ranked 49th with F1-score of 0.82. This result showed to be competitive as it only under-performed the best system by 0.052{\%}F1-score.",
}

We present the findings of our participation in the SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS) task, a shared task on offensive language (sexism) detection on English Gab and Reddit dataset. We investigated the effects of transferring two language models: XLM-T (sentiment classification) and HateBERT (same domain - Reddit) for multilevel classification into Sexist or not Sexist, and other subsequent sub-classifications of the sexist data. We also use synthetic classification of unlabelled dataset and intermediary class information to maximize the performance of our models. We submitted a system in Task A, and it ranked 49th with F1-score of 0.82. This result showed to be competitive as it only under-performed the best system by 0.052{\%}F1-score.
Aliyu, Saminu Mohammad Abdulmumin, Idris Muhammad, Shamsuddeen Hassan Ahmad, Ibrahim Said Salahudeen, Saheed Abdullahi Yusuf, Aliyu Lawan, Falalu Ibrahim
HausaNLP at SemEval-2023 Task 10: Transfer Learning, Synthetic Data and Side-information for Multi-level Sexism Classification