@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T BanHADEX: Towards Explainable HAte Speech Detection in Bangla Using Human Annotated EXplanation
%A Raquib, Faisal Hossain
%A Mazumder, Akm Moshiur Rahman
%A Fahim, Md
%A Fuad, Md Tahmid Hasan
%A Ishmam, Md Farhan
%A Sultana, Faria
%A Amin, M. Ashraful
%A Ali, Amin Ahsan
%A Rahman, Akmmahbubur
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F raquib-etal-2026-banhadex
%X 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.
%U https://aclanthology.org/2026.acl-long.2022/
%P 43652-43674
Markdown (Informal)
[BanHADEX: Towards Explainable HAte Speech Detection in Bangla Using Human Annotated EXplanation](https://aclanthology.org/2026.acl-long.2022/) (Raquib et al., ACL 2026)
ACL
- Faisal Hossain Raquib, Akm Moshiur Rahman Mazumder, Md Fahim, Md Tahmid Hasan Fuad, Md Farhan Ishmam, Faria Sultana, M Ashraful Amin, Amin Ahsan Ali, and Akmmahbubur Rahman. 2026. BanHADEX: Towards Explainable HAte Speech Detection in Bangla Using Human Annotated EXplanation. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 43652–43674, San Diego, California, United States. Association for Computational Linguistics.