@inproceedings{kumar-etal-2026-indilangtech,
title = "{I}ndi{L}ang{T}ech@{D}ravidian{L}ang{T}ech 2026: Hierarchical Modeling for Multi-Level Political Meme Classification",
author = "Kumar, Saurabh and
G, Vivekananda and
Sanasam, Ranbir Singh and
Nandi, Sukumar",
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-1.41/",
pages = "273--278",
ISBN = "979-8-89176-401-9",
abstract = "Political memes are a widely used form of digital political expression in linguistically diverse regions such as South India, where visual cues, textual overlays, and cultural symbolism convey complex political narratives. The Shared Task on Multi-Level Political Meme Classification at DravidianLangTech 2026 introduces a hierarchical setting requiring stance identification (Support vs. Troll) and target-type prediction (Individual vs. Party) for Tamil and Malayalam memes. We propose a two-stage hierarchical framework based on the Gemma 3 4B Instruction model. Instead of jointly predicting both levels, two specialized models are fine-tuned: the first predicts meme stance, and its output conditions the second model for target identification, explicitly modeling the dependency between the meme content, the predicted stance, and the target type. Using LoRA-based parameter-efficient instruction tuning, our approach achieves an average F1-scores of 0.8029 for Tamil and 0.6950 for Malayalam across both levels, ranking 1st in Tamil and 4th in Malayalam."
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<abstract>Political memes are a widely used form of digital political expression in linguistically diverse regions such as South India, where visual cues, textual overlays, and cultural symbolism convey complex political narratives. The Shared Task on Multi-Level Political Meme Classification at DravidianLangTech 2026 introduces a hierarchical setting requiring stance identification (Support vs. Troll) and target-type prediction (Individual vs. Party) for Tamil and Malayalam memes. We propose a two-stage hierarchical framework based on the Gemma 3 4B Instruction model. Instead of jointly predicting both levels, two specialized models are fine-tuned: the first predicts meme stance, and its output conditions the second model for target identification, explicitly modeling the dependency between the meme content, the predicted stance, and the target type. Using LoRA-based parameter-efficient instruction tuning, our approach achieves an average F1-scores of 0.8029 for Tamil and 0.6950 for Malayalam across both levels, ranking 1st in Tamil and 4th in Malayalam.</abstract>
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%0 Conference Proceedings
%T IndiLangTech@DravidianLangTech 2026: Hierarchical Modeling for Multi-Level Political Meme Classification
%A Kumar, Saurabh
%A G, Vivekananda
%A Sanasam, Ranbir Singh
%A Nandi, Sukumar
%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 kumar-etal-2026-indilangtech
%X Political memes are a widely used form of digital political expression in linguistically diverse regions such as South India, where visual cues, textual overlays, and cultural symbolism convey complex political narratives. The Shared Task on Multi-Level Political Meme Classification at DravidianLangTech 2026 introduces a hierarchical setting requiring stance identification (Support vs. Troll) and target-type prediction (Individual vs. Party) for Tamil and Malayalam memes. We propose a two-stage hierarchical framework based on the Gemma 3 4B Instruction model. Instead of jointly predicting both levels, two specialized models are fine-tuned: the first predicts meme stance, and its output conditions the second model for target identification, explicitly modeling the dependency between the meme content, the predicted stance, and the target type. Using LoRA-based parameter-efficient instruction tuning, our approach achieves an average F1-scores of 0.8029 for Tamil and 0.6950 for Malayalam across both levels, ranking 1st in Tamil and 4th in Malayalam.
%U https://aclanthology.org/2026.dravidianlangtech-1.41/
%P 273-278
Markdown (Informal)
[IndiLangTech@DravidianLangTech 2026: Hierarchical Modeling for Multi-Level Political Meme Classification](https://aclanthology.org/2026.dravidianlangtech-1.41/) (Kumar et al., DravidianLangTech 2026)
ACL