@inproceedings{rajalakshmi-etal-2025-dlrg,
title = "{DLRG}@{D}ravidian{L}ang{T}ech 2025: Multimodal Hate Speech Detection in {D}ravidian Languages",
author = "Rajalakshmi, Ratnavel and
Kannan, Ramesh and
Saini, Meetesh and
Mallik, Bitan",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Rajiakodi, Saranya and
Palani, Balasubramanian and
Subramanian, Malliga and
Cn, Subalalitha and
Chinnappa, Dhivya",
booktitle = "Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = may,
year = "2025",
address = "Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.dravidianlangtech-1.67/",
doi = "10.18653/v1/2025.dravidianlangtech-1.67",
pages = "376--380",
ISBN = "979-8-89176-228-2",
abstract = "Social media is a powerful communication tooland rich in diverse content requiring innovativeapproaches to understand nuances of the lan-guages. Addressing challenges like hate speechnecessitates multimodal analysis that integratestextual, and other cues to capture its contextand intent effectively. This paper proposes amultimodal hate speech detection system inTamil, which uses textual and audio featuresfor classification. Our proposed system usesa fine-tuned Indic-BERT model for text basedhate speech detection and Wav2Vec2 modelfor audio based hate speech detection of au-dio data. The fine-tuned Indic-BERT modelwith Whisper achieved an F1 score of 0.25 onMultimodal approach. Our proposed approachranked at 10th position in the shared task onMultimodal Hate Speech Detection in Dravid-ian languages at the NAACL 2025 WorkshopDravidianLangTech."
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%0 Conference Proceedings
%T DLRG@DravidianLangTech 2025: Multimodal Hate Speech Detection in Dravidian Languages
%A Rajalakshmi, Ratnavel
%A Kannan, Ramesh
%A Saini, Meetesh
%A Mallik, Bitan
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Rajiakodi, Saranya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Cn, Subalalitha
%Y Chinnappa, Dhivya
%S Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2025
%8 May
%I Association for Computational Linguistics
%C Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico
%@ 979-8-89176-228-2
%F rajalakshmi-etal-2025-dlrg
%X Social media is a powerful communication tooland rich in diverse content requiring innovativeapproaches to understand nuances of the lan-guages. Addressing challenges like hate speechnecessitates multimodal analysis that integratestextual, and other cues to capture its contextand intent effectively. This paper proposes amultimodal hate speech detection system inTamil, which uses textual and audio featuresfor classification. Our proposed system usesa fine-tuned Indic-BERT model for text basedhate speech detection and Wav2Vec2 modelfor audio based hate speech detection of au-dio data. The fine-tuned Indic-BERT modelwith Whisper achieved an F1 score of 0.25 onMultimodal approach. Our proposed approachranked at 10th position in the shared task onMultimodal Hate Speech Detection in Dravid-ian languages at the NAACL 2025 WorkshopDravidianLangTech.
%R 10.18653/v1/2025.dravidianlangtech-1.67
%U https://aclanthology.org/2025.dravidianlangtech-1.67/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.67
%P 376-380
Markdown (Informal)
[DLRG@DravidianLangTech 2025: Multimodal Hate Speech Detection in Dravidian Languages](https://aclanthology.org/2025.dravidianlangtech-1.67/) (Rajalakshmi et al., DravidianLangTech 2025)
ACL
- Ratnavel Rajalakshmi, Ramesh Kannan, Meetesh Saini, and Bitan Mallik. 2025. DLRG@DravidianLangTech 2025: Multimodal Hate Speech Detection in Dravidian Languages. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 376–380, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.