@inproceedings{adam-etal-2026-team-hausanlp,
    title = "Team {H}ausa{NLP} at {S}em{E}val-2026 Task 9: Tackling Class Imbalance in Low-Resource {H}ausa Polarization Detection",
    author = "Adam, Faisal  and
      Aji, Sani  and
      Aliyu, Lukman  and
      Abubakar, Abdulhamid",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.8/",
    pages = "54--58",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes our submission toSemEval-2026 Task 9, Subtask 2 (Hausa). Thetask involves identifying specific categories ofpolarization (Political, Religious, Ethnic, etc.)in Hausa social media comments. The datasetpresented significant challenges, primarily extreme class imbalance and the low-resourcenature of the language. Our system uses a pretrained multilingual transformer (Afro-XLMRLarge) fine-tuned with Weighted Binary CrossEntropy loss and dynamic undersampling (1:3ratio) to mitigate the scarcity of polarized examples. On the official test set, our systemachieved an official Macro-F1 score of 0.2346and a Micro-F1 score of 0.2581. Our model isrecall-oriented (Micro-Recall: 0.6166), demonstrating strong capability in detecting polarization, though precision remains a challenge(0.1632). We achieved our best per-class performance in the Political domain (F1: 0.48)."
}