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    <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.</abstract>
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%0 Conference Proceedings
%T Overview of BLP-2025 Task 1: Bangla Hate Speech Identification
%A Hasan, Md Arid
%A Alam, Firoj
%A Hossain, Md Fahad
%A Naseem, Usman
%A Ahmed, Syed Ishtiaque
%Y Alam, Firoj
%Y Kar, Sudipta
%Y Chowdhury, Shammur Absar
%Y Hassan, Naeemul
%Y Prince, Enamul Hoque
%Y Tasnim, Mohiuddin
%Y Rony, Md Rashad Al Hasan
%Y Rahman, Md Tahmid Rahman
%S Proceedings of the Second Workshop on Bangla Language Processing (BLP-2025)
%D 2025
%8 December
%I Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-314-2
%F hasan-etal-2025-overview
%X 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.
%U https://aclanthology.org/2025.banglalp-1.32/
%P 388-401

Markdown (Informal)

[Overview of BLP-2025 Task 1: Bangla Hate Speech Identification](https://aclanthology.org/2025.banglalp-1.32/) (Hasan et al., BanglaLP 2025)

• Overview of BLP-2025 Task 1: Bangla Hate Speech Identification (Hasan et al., BanglaLP 2025)

ACL

• Md Arid Hasan, Firoj Alam, Md Fahad Hossain, Usman Naseem, and Syed Ishtiaque Ahmed. 2025. Overview of BLP-2025 Task 1: Bangla Hate Speech Identification. In Proceedings of the Second Workshop on Bangla Language Processing (BLP-2025), pages 388–401, Mumbai, India. Association for Computational Linguistics.