@inproceedings{alam-etal-2025-cou,
    title = "{C}o{U}-{CU}-{DSG} at {BLP}-2025 Task 1: Leveraging Weighted Probabilistic Fusion of Language Models for {B}angla Hate Speech Detection",
    author = "Alam, Ashraful  and
      Aziz, Abdul  and
      Chy, Abu Nowshed",
    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-1.44/",
    pages = "492--497",
    ISBN = "979-8-89176-314-2",
    abstract = "The upsurge of social media and open source platforms has created new avenues for the rapid, global spread of negativity and obscenities targeting individuals and organizations. The process to identify hate speech is critical for the lexical and regional variation as well as the morphological complexity of the texts, especially in low-resource languages, e.g. Bangla. This paper presents our participation in the Hate Speech Detection task at the second workshop on Bangla Language Processing. The objective of this task is not only to detect whether the content is hateful, but also to identify the type of hate, the target group, and its severity. We proposed a Transformer-based weighted probabilistic fusion model to detect the presence of hate speech in Bangla texts. We independently fine-tuned three pre-trained Transformer models, BanglaBERT, XLM-RoBERTa, and MuRIL, to capture diverse linguistic representations. The probability distributions obtained from each model were combined using a weighted fusion strategy, allowing the system to leverage the strengths of all models simultaneously. This fused representation was then used to predict the final labels for the given instances. The experimental results showed that our proposed method obtained competitive performance, ranking 10th in subtask 1A and 15th in subtask 1B among the participants."
}