@inproceedings{saha-etal-2025-retriv,
    title = "Retriv at {BLP}-2025 Task 1: A Transformer Ensemble and Multi-Task Learning Approach for {B}angla Hate Speech Identification",
    author = "Saha, Sourav  and
      Asib, K M Nafi  and
      Hoque, Mohammed Moshiul",
    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-1.43/",
    pages = "485--491",
    ISBN = "979-8-89176-314-2",
    abstract = "This paper addresses the problem of Bangla hate speech identification, a socially impactful yet linguistically challenging task. As part of the Bangla Multi-task Hate Speech Identification shared task at the BLP Workshop, IJCNLP-AACL 2025, we participated in all three subtasks: (1A) hate type classification, (1B) target group identification, and (1C) joint detection of type, severity, and target. For subtasks 1A and 1B, we employed a soft-voting ensemble of transformer models (BanglaBERT, MuRIL, IndicBERTv2). For subtask 1C, we trained three multitask variants and aggregated their predictions through a weighted voting ensemble. Our systems achieved micro-$f_{1}$ scores of 72.75{\%} (1A) and 72.69{\%} (1B), and a weighted micro-$f_{1}$ score of 72.62{\%} (1C). On the shared task leaderboard, these corresponded to 9th, 10th, and 7th positions, respectively. These results highlight the promise of transformer ensembles and weighted multitask frameworks for advancing Bangla hate speech detection in low-resource contexts. We made experimental scripts publicly available for the community."
}