@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://preview.aclanthology.org/ingest-ijcnlp-aacl/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."
}