@inproceedings{islam-etal-2025-code,
    title = "{C}ode{\_}{G}en at {BLP}-2025 Task 1: Enhancing {B}angla Hate Speech Detection with Transformers through Token-Aware Adversarial Contrastive Training and Layer-wise Learning Rate Decay",
    author = "Islam, Shifat  and
      Agarwala, Abhishek  and
      Ghosh, Emon",
    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-1.47/",
    pages = "513--522",
    ISBN = "979-8-89176-314-2",
    abstract = "Bangla social media contains several types of hate speech and slurs, but automatic detection is tough due to linguistic complexity, data imbalance and limited resources. We address this challenge in the BLP-2025 shared task by combining Token-Aware Adversarial Contrastive Training (TACT) with Layer-wise Learning Rate Decay (LLRD) to fine-tune transformer models like BanglaBERT, MuRIL, mE5-base and Twitter XLM-R. To capture the complementary strengths of each model, we aggregate the model outputs through logits ensembling and get a robust system for multiclass classification. On the official test set, our model achieved F1 scores of 0.7362 for hate type, 0.7335 for severity, and 0.7361 for target ranking, placing it 1st, 2nd, and 3rd, respectively. The findings indicate that adversarial fine-tuning with logits ensemble learning is a robust way to detect hate speech in resource-limited languages and provides valuable insights for multilingual and low-resource NLP research."
}