@inproceedings{raquib-etal-2026-banhadex,
    title = "{B}an{HADEX}: Towards Explainable {HA}te Speech Detection in {B}angla Using Human Annotated {EX}planation",
    author = "Raquib, Faisal Hossain  and
      Mazumder, Akm Moshiur Rahman  and
      Fahim, Md  and
      Fuad, Md Tahmid Hasan  and
      Ishmam, Md Farhan  and
      Sultana, Faria  and
      Amin, M Ashraful  and
      Ali, Amin Ahsan  and
      Rahman, Akmmahbubur",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.2022/",
    pages = "43652--43674",
    ISBN = "979-8-89176-390-6",
    abstract = "Online safety in low-resource languages hinges not only on accurate hate speech detection but also on transparent, culturally grounded explanations. Yet prior works in Bangla largely focus on hate classification, while overlooking interpretability. We address this gap by introducing BanHADEX, the first hate explainability dataset in Bangla with human-annotated labels. BanHADEX contains 19,203 YouTube comments spanning April 2024{--}June 2025, annotated for binary hate classification with seven fine-grained hate categories, seven target groups, and concise explanations for each sample. Our data pipeline relies on a two-stage annotation protocol that uses majority voting for robust labeling. Our rich suite of experiments on open and closed-source LLMs reveals that explanation-guided LoRA substantially outperforms both classification and explanation quality across prompting and fine-tuning strategies. BanHADEX establishes the groundworks for faithful interpretability and safer moderation in linguistically rich yet under-resourced languages."
}