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