@inproceedings{hasan-etal-2025-overview,
    title = "Overview of {BLP}-2025 Task 1: {B}angla Hate Speech Identification",
    author = "Hasan, Md Arid  and
      Alam, Firoj  and
      Hossain, Md Fahad  and
      Naseem, Usman  and
      Ahmed, Syed Ishtiaque",
    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-1.32/",
    pages = "388--401",
    ISBN = "979-8-89176-314-2",
    abstract = "Online discourse in Bangla is rife with nuanced toxicity expressed through code-mixing, dialectal variation, and euphemism. Effective moderation thus requires fine-grained detection of hate type, target, and severity, rather than a binary label. To address this, we organized the Bangla Hate Speech Identification Shared Task at the BLP 2025 workshop, co-located with IJCNLP-AACL 2025, comprising three subtasks: (1A) hate-type detection, (1B) hate-target detection, and (1C) joint prediction of type, target, and severity in a multi-task setup. The subtasks attracted 161, 103, and 90 participants, with 36, 23, and 20 final submissions, respectively, while a total of 19 teams submitted system description papers. The submitted systems employed a wide range of approaches, ranging from classical machine learning to fine-tuned pretrained models and zero-/few-shot LLMs. We describe the task setup, datasets, and evaluation framework, and summarize participant systems. All datasets and evaluation scripts are publicly released."
}