@inproceedings{tripty-etal-2025-belite,
title = "{BE}lite at {BLP}-2025 Task 1: Leveraging Ensemble for Multi Task Hate Speech Detection in {B}angla",
author = "Tripty, Zannatul Fardaush and
Adib, Ibnul Mohammad and
Fahad, Nafiz and
Hussain, Muhammad Tanjib and
Morol, Md Kishor",
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-1.36/",
pages = "421--429",
ISBN = "979-8-89176-314-2",
abstract = "The widespread use of the internet has made sharing information on social media more convenient. At the same time, it provides a platform for individuals with malicious intent to easily spread hateful content. Since many users prefer to communicate in their native language, detecting hate speech in Bengali poses a significant challenge. This study aims to identify Bengali hate speech on social media platforms. A shared task on Bengali hate speech detection was organized by the Second Bangla Language Processing Workshop (BLP). To tackle this task, we implemented five traditional machine learning models (LR, SVM, RF, NB, XGB), three deep learning models (CNN, BiLSTM, CNN+BiLSTM), and three transformer-based models (Bangla-BERT, m-BERT, XLM-R). Among all models, a weighted ensemble of transformer models achieved the best performance.Our approach ranked \textbf{ \textit{3rd}} in Subtask 1A with a micro-\textit{F1} score of 0.734, \textbf{ \textit{6th}} in Subtask 1B with 0.7315, and, after post-competition experiments, \textbf{ \textit{4th}} in Subtask 1C with 0.735."
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<abstract>The widespread use of the internet has made sharing information on social media more convenient. At the same time, it provides a platform for individuals with malicious intent to easily spread hateful content. Since many users prefer to communicate in their native language, detecting hate speech in Bengali poses a significant challenge. This study aims to identify Bengali hate speech on social media platforms. A shared task on Bengali hate speech detection was organized by the Second Bangla Language Processing Workshop (BLP). To tackle this task, we implemented five traditional machine learning models (LR, SVM, RF, NB, XGB), three deep learning models (CNN, BiLSTM, CNN+BiLSTM), and three transformer-based models (Bangla-BERT, m-BERT, XLM-R). Among all models, a weighted ensemble of transformer models achieved the best performance.Our approach ranked 3rd in Subtask 1A with a micro-F1 score of 0.734, 6th in Subtask 1B with 0.7315, and, after post-competition experiments, 4th in Subtask 1C with 0.735.</abstract>
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%0 Conference Proceedings
%T BElite at BLP-2025 Task 1: Leveraging Ensemble for Multi Task Hate Speech Detection in Bangla
%A Tripty, Zannatul Fardaush
%A Adib, Ibnul Mohammad
%A Fahad, Nafiz
%A Hussain, Muhammad Tanjib
%A Morol, Md Kishor
%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 tripty-etal-2025-belite
%X The widespread use of the internet has made sharing information on social media more convenient. At the same time, it provides a platform for individuals with malicious intent to easily spread hateful content. Since many users prefer to communicate in their native language, detecting hate speech in Bengali poses a significant challenge. This study aims to identify Bengali hate speech on social media platforms. A shared task on Bengali hate speech detection was organized by the Second Bangla Language Processing Workshop (BLP). To tackle this task, we implemented five traditional machine learning models (LR, SVM, RF, NB, XGB), three deep learning models (CNN, BiLSTM, CNN+BiLSTM), and three transformer-based models (Bangla-BERT, m-BERT, XLM-R). Among all models, a weighted ensemble of transformer models achieved the best performance.Our approach ranked 3rd in Subtask 1A with a micro-F1 score of 0.734, 6th in Subtask 1B with 0.7315, and, after post-competition experiments, 4th in Subtask 1C with 0.735.
%U https://aclanthology.org/2025.banglalp-1.36/
%P 421-429
Markdown (Informal)
[BElite at BLP-2025 Task 1: Leveraging Ensemble for Multi Task Hate Speech Detection in Bangla](https://aclanthology.org/2025.banglalp-1.36/) (Tripty et al., BanglaLP 2025)
ACL