@inproceedings{hoque-etal-2025-gradient,
    title = "Gradient Masters at {BLP}-2025 Task 1: Advancing Low-Resource {NLP} for {B}engali using Ensemble-Based Adversarial Training for Hate Speech Detection",
    author = "Hoque, Syed Mohaiminul  and
      Rahman, Naimur  and
      Hossain, Md Sakhawat",
    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-1.34/",
    pages = "408--413",
    ISBN = "979-8-89176-314-2",
    abstract = "This paper introduces the approach of ``Gradient Masters'' for BLP-2025 Task 1: ``Bangla Multitask Hate Speech Identification Shared Task''. We present an ensemble-based fine-tuning strategy for addressing subtasks 1A (hate-type classification) and 1B (target group classification) in YouTube comments. We propose a hybrid approach on a Bangla Language Model, which outperformed the baseline models and secured the 6th position in subtask 1A with a micro F1 score of 73.23{\%} and the third position in subtask 1B with 73.28{\%}. We conducted extensive experiments that evaluated the robustness of the model throughout the development and evaluation phases, including comparisons with other Language Model variants, to measure generalization in low-resource Bangla hate speech scenarios and data set coverage. In addition, we provide a detailed analysis of our findings, exploring misclassification patterns in the detection of hate speech."
}