@inproceedings{hossan-etal-2025-cuet-nlp-zenith,
title = "{CUET}-{NLP}{\_}{Z}enith at {BLP}-2025 Task 1: A Multi-Task Ensemble Approach for Detecting Hate Speech in {B}engali {Y}ou{T}ube Comments",
author = "Hossan, Md. Refaj and
Ahmed, Kawsar 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://aclanthology.org/2025.banglalp-1.38/",
pages = "443--452",
ISBN = "979-8-89176-314-2",
abstract = "Hate speech on social media platforms, particularly in low-resource languages like Bengali, poses a significant challenge due to its nuanced nature and the need to understand its type, severity, and targeted group. To address this, the Bangla Multi-task Hate Speech Identification Shared Task at BLP 2025 adopts a multi-task learning framework that requires systems to classify Bangla YouTube comments across three subtasks simultaneously: type of hate, severity, and targeted group. To tackle these challenges, this work presents BanTriX, a transformer ensemble method that leverages BanglaBERT-I, XLM-R, and BanglaBERT-II. Evaluation results show that the BanTriX, optimized with cross-entropy loss, achieves the highest weighted micro F1-score of 73.78{\%} in Subtask 1C, securing our team 2nd place in the shared task."
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<abstract>Hate speech on social media platforms, particularly in low-resource languages like Bengali, poses a significant challenge due to its nuanced nature and the need to understand its type, severity, and targeted group. To address this, the Bangla Multi-task Hate Speech Identification Shared Task at BLP 2025 adopts a multi-task learning framework that requires systems to classify Bangla YouTube comments across three subtasks simultaneously: type of hate, severity, and targeted group. To tackle these challenges, this work presents BanTriX, a transformer ensemble method that leverages BanglaBERT-I, XLM-R, and BanglaBERT-II. Evaluation results show that the BanTriX, optimized with cross-entropy loss, achieves the highest weighted micro F1-score of 73.78% in Subtask 1C, securing our team 2nd place in the shared task.</abstract>
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%0 Conference Proceedings
%T CUET-NLP_Zenith at BLP-2025 Task 1: A Multi-Task Ensemble Approach for Detecting Hate Speech in Bengali YouTube Comments
%A Hossan, Md. Refaj
%A Ahmed, Kawsar
%A Hoque, Mohammed Moshiul
%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 hossan-etal-2025-cuet-nlp-zenith
%X Hate speech on social media platforms, particularly in low-resource languages like Bengali, poses a significant challenge due to its nuanced nature and the need to understand its type, severity, and targeted group. To address this, the Bangla Multi-task Hate Speech Identification Shared Task at BLP 2025 adopts a multi-task learning framework that requires systems to classify Bangla YouTube comments across three subtasks simultaneously: type of hate, severity, and targeted group. To tackle these challenges, this work presents BanTriX, a transformer ensemble method that leverages BanglaBERT-I, XLM-R, and BanglaBERT-II. Evaluation results show that the BanTriX, optimized with cross-entropy loss, achieves the highest weighted micro F1-score of 73.78% in Subtask 1C, securing our team 2nd place in the shared task.
%U https://aclanthology.org/2025.banglalp-1.38/
%P 443-452
Markdown (Informal)
[CUET-NLP_Zenith at BLP-2025 Task 1: A Multi-Task Ensemble Approach for Detecting Hate Speech in Bengali YouTube Comments](https://aclanthology.org/2025.banglalp-1.38/) (Hossan et al., BanglaLP 2025)
ACL