@inproceedings{a-kumar-2026-nitc,
title = "{NITC}-{HSR}@{D}ravidian{L}ang{T}ech 2026: Ensembling Multilingual Transformer Models for Detecting Abusive {T}amil Text Targeting Women on Social Media",
author = "A, Rameez Mohammed and
Kumar, S D Madhu",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Rajiakodi, Saranya and
Navaneethakrishnan, Subalalitha and
Chinnappa, Dhivya and
Palani, Balasubramanian and
Subramanian, Malliga and
Shanmugavadivel, Kogilavani and
Rajalakshmi, Ratnavel",
booktitle = "Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for {D}ravidian Languages",
month = jul,
year = "2026",
address = "Underline (Virtual)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.dravidianlangtech-1.48/",
pages = "316--320",
ISBN = "979-8-89176-401-9",
abstract = "The proliferation of misogynistic content on social media platforms is a serious problem that requires the development of automated detection systems, which is a challenging task for low-resource languages like Tamil. This study investigates the effectiveness of multilingual transformer models for identifying abusive Tamil text targeting women in social media. Results indicate that such models achieve strong baseline performance on this task. Furthermore, an ensemble of two best performing models was found to improve the classification performance further. The results also highlighted the significance of domain-specific pre-training for improving classifier performance. The best performing ensemble model achieved a weighted F1 score of 0.83 on the test set, placing our approach in first position in the shared task."
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%0 Conference Proceedings
%T NITC-HSR@DravidianLangTech 2026: Ensembling Multilingual Transformer Models for Detecting Abusive Tamil Text Targeting Women on Social Media
%A A, Rameez Mohammed
%A Kumar, S. D. Madhu
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Rajiakodi, Saranya
%Y Navaneethakrishnan, Subalalitha
%Y Chinnappa, Dhivya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Shanmugavadivel, Kogilavani
%Y Rajalakshmi, Ratnavel
%S Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2026
%8 July
%I Association for Computational Linguistics
%C Underline (Virtual)
%@ 979-8-89176-401-9
%F a-kumar-2026-nitc
%X The proliferation of misogynistic content on social media platforms is a serious problem that requires the development of automated detection systems, which is a challenging task for low-resource languages like Tamil. This study investigates the effectiveness of multilingual transformer models for identifying abusive Tamil text targeting women in social media. Results indicate that such models achieve strong baseline performance on this task. Furthermore, an ensemble of two best performing models was found to improve the classification performance further. The results also highlighted the significance of domain-specific pre-training for improving classifier performance. The best performing ensemble model achieved a weighted F1 score of 0.83 on the test set, placing our approach in first position in the shared task.
%U https://aclanthology.org/2026.dravidianlangtech-1.48/
%P 316-320
Markdown (Informal)
[NITC-HSR@DravidianLangTech 2026: Ensembling Multilingual Transformer Models for Detecting Abusive Tamil Text Targeting Women on Social Media](https://aclanthology.org/2026.dravidianlangtech-1.48/) (A & Kumar, DravidianLangTech 2026)
ACL