Team HausaNLP at SemEval-2026 Task 9: Tackling Class Imbalance in Low-Resource Hausa Polarization Detection

AbstractThis 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).