@inproceedings{nabil-etal-2025-nsu,
title = "{NSU}{\_}{MIL}ab at {BLP}-2025 Task 1: Decoding {B}angla Hate Speech: Fine-Grained Type and Target Detection via Transformer Ensembles",
author = "Nabil, Md. Mohibur Rahman and
Kabir, Muhammad Rafsan and
Islam, Rakib and
Rahman, Fuad and
Mohammed, Nabeel and
Rahman, Shafin",
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://aclanthology.org/2025.banglalp-1.40/",
pages = "461--467",
ISBN = "979-8-89176-314-2",
abstract = "This paper describes our participation in Task 1A and Task 1B of the Task 1A and Task 1B of the BLP Workshop, focused on Bangla Multi-task Hatespeech Identification. Our approach involves systematic evaluation of four transformer models: BanglaBERT, XLM-RoBERTa, IndicBERT, and Bengali Abusive MuRIL. To enhance performance, we implemented an ensemble strategy that averages output probabilities from these transformer models, which consistently outperformed individual models across both tasks. The baseline classical methods demonstrated limitations in capturing complex linguistic cues, underscoring the superiority of transformer-based approaches for low-resource hate speech detection. Our solution initially achieved F1 scores of 0.7235 (ranked 12th) for Task 1A and 0.6981 (ranked 17th) for Task 1B among participating teams. Through post-competition refinements, we improved our Task 1B performance to 0.7331, demonstrating the effectiveness of ensemble methods in Bangla hate speech detection."
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<abstract>This paper describes our participation in Task 1A and Task 1B of the Task 1A and Task 1B of the BLP Workshop, focused on Bangla Multi-task Hatespeech Identification. Our approach involves systematic evaluation of four transformer models: BanglaBERT, XLM-RoBERTa, IndicBERT, and Bengali Abusive MuRIL. To enhance performance, we implemented an ensemble strategy that averages output probabilities from these transformer models, which consistently outperformed individual models across both tasks. The baseline classical methods demonstrated limitations in capturing complex linguistic cues, underscoring the superiority of transformer-based approaches for low-resource hate speech detection. Our solution initially achieved F1 scores of 0.7235 (ranked 12th) for Task 1A and 0.6981 (ranked 17th) for Task 1B among participating teams. Through post-competition refinements, we improved our Task 1B performance to 0.7331, demonstrating the effectiveness of ensemble methods in Bangla hate speech detection.</abstract>
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%0 Conference Proceedings
%T NSU_MILab at BLP-2025 Task 1: Decoding Bangla Hate Speech: Fine-Grained Type and Target Detection via Transformer Ensembles
%A Nabil, Md. Mohibur Rahman
%A Kabir, Muhammad Rafsan
%A Islam, Rakib
%A Rahman, Fuad
%A Mohammed, Nabeel
%A Rahman, Shafin
%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 nabil-etal-2025-nsu
%X This paper describes our participation in Task 1A and Task 1B of the Task 1A and Task 1B of the BLP Workshop, focused on Bangla Multi-task Hatespeech Identification. Our approach involves systematic evaluation of four transformer models: BanglaBERT, XLM-RoBERTa, IndicBERT, and Bengali Abusive MuRIL. To enhance performance, we implemented an ensemble strategy that averages output probabilities from these transformer models, which consistently outperformed individual models across both tasks. The baseline classical methods demonstrated limitations in capturing complex linguistic cues, underscoring the superiority of transformer-based approaches for low-resource hate speech detection. Our solution initially achieved F1 scores of 0.7235 (ranked 12th) for Task 1A and 0.6981 (ranked 17th) for Task 1B among participating teams. Through post-competition refinements, we improved our Task 1B performance to 0.7331, demonstrating the effectiveness of ensemble methods in Bangla hate speech detection.
%U https://aclanthology.org/2025.banglalp-1.40/
%P 461-467
Markdown (Informal)
[NSU_MILab at BLP-2025 Task 1: Decoding Bangla Hate Speech: Fine-Grained Type and Target Detection via Transformer Ensembles](https://aclanthology.org/2025.banglalp-1.40/) (Nabil et al., BanglaLP 2025)
ACL