@inproceedings{mahim-hasan-2025-teamhatemate,
    title = "{T}eam{H}ate{M}ate at {BLP} Task1: Divide and Conquer: A Two-Stage Cascaded Framework with K-Fold Ensembling for Multi-Label {B}angla Hate Speech Classification",
    author = "Mahim, Mahbub Islam  and
      Hasan, Mehedi",
    editor = "Alam, Firoj  and
      Kar, Sudipta  and
      Chowdhury, Shammur Absar  and
      Hassan, Naeemul  and
      Prince, Enamul Hoque  and
      Tasnim, Mohiuddin  and
      Rony, Md Rashad Al Hasan  and
      Rahman, Md Tahmid Rahman",
    booktitle = "Proceedings of the Second Workshop on Bangla Language Processing (BLP-2025)",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.banglalp-1.39/",
    pages = "453--460",
    ISBN = "979-8-89176-314-2",
    abstract = "Detecting hate speech on social media is essential for safeguarding online communities, yet it remains challenging for low-resource languages like Bangla due to class imbalance and subjective annotations. We introduce a two-stage cascaded framework with $k$-fold ensembling to address the BLP Workshop 2025 Shared Task{'}s three subtasks: 1A (hate type classification), 1B (target identification), and 1C (joint classification of type, target, and severity). Our solution balances precision and recall, achieving micro-$F1$ scores of 0.7331 on 1A, 0.7356 on 1B, and 0.7392 on 1C, ranking 4th on 1A and 1st on both 1B and 1C. It performs strongly on major classes, although underrepresented labels such as sexism and mild severity remain challenging. Our method makes the optimal use of limited data through $k$-fold ensembling and delivers overall balanced performance across majority and minority classes by mitigating class imbalance via cascaded layers."
}