@inproceedings{kp-etal-2026-synapse,
title = "{SYNAPSE}@{D}ravidian{L}ang{T}ech 2026: Multi-Level Political Meme Classification for {T}amil and {M}alayalam",
author = "KP, Suriya and
K, Durai Singh and
G, Gnanasabesan and
S, Ganesh Sundhar and
N, Hari Krishnan and
G, Jyothish Lal",
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-1.60/",
pages = "381--386",
ISBN = "979-8-89176-401-9",
abstract = "Political memes in Tamil and Malayalampresent unique multimodal challenges for automated under-standing, combining visual context with code-mixed, cultur-ally grounded text. We present SYNAPSE, our system forthe DravidianLangTech@ACL 2026 shared task on multi-levelpolitical meme classification. The task requires hierarchicalclassification of memes along two levels: Level 1 identifies thepolitical stance (Support/Praise vs. Troll/Oppose), and Level 2identifies the target (individual person vs. party). Our approachfine-tunes the Qwen3-VL-2B-Instruct vision-language modelusing parameter-efficient LoRA adapters on task-specific mul-timodal data, with structured output prompting for hierarchi-cal label prediction. We report results for both Tamil andMalayalam subtracks. For Malayalam, our system achievesa Level 1 F1 of 0.9200 and Level 2 F1 of 0.4256 (Avg-F1:0.6728, Rank 5). For Tamil, our system achieves a Level 1 F1of 0.7840 and Level 2 F1 of 0.4885 (Avg-F1: 0.6362, Rank 14)."
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<abstract>Political memes in Tamil and Malayalampresent unique multimodal challenges for automated under-standing, combining visual context with code-mixed, cultur-ally grounded text. We present SYNAPSE, our system forthe DravidianLangTech@ACL 2026 shared task on multi-levelpolitical meme classification. The task requires hierarchicalclassification of memes along two levels: Level 1 identifies thepolitical stance (Support/Praise vs. Troll/Oppose), and Level 2identifies the target (individual person vs. party). Our approachfine-tunes the Qwen3-VL-2B-Instruct vision-language modelusing parameter-efficient LoRA adapters on task-specific mul-timodal data, with structured output prompting for hierarchi-cal label prediction. We report results for both Tamil andMalayalam subtracks. For Malayalam, our system achievesa Level 1 F1 of 0.9200 and Level 2 F1 of 0.4256 (Avg-F1:0.6728, Rank 5). For Tamil, our system achieves a Level 1 F1of 0.7840 and Level 2 F1 of 0.4885 (Avg-F1: 0.6362, Rank 14).</abstract>
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%0 Conference Proceedings
%T SYNAPSE@DravidianLangTech 2026: Multi-Level Political Meme Classification for Tamil and Malayalam
%A KP, Suriya
%A K, Durai Singh
%A G, Gnanasabesan
%A S, Ganesh Sundhar
%A N, Hari Krishnan
%A G, Jyothish Lal
%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 kp-etal-2026-synapse
%X Political memes in Tamil and Malayalampresent unique multimodal challenges for automated under-standing, combining visual context with code-mixed, cultur-ally grounded text. We present SYNAPSE, our system forthe DravidianLangTech@ACL 2026 shared task on multi-levelpolitical meme classification. The task requires hierarchicalclassification of memes along two levels: Level 1 identifies thepolitical stance (Support/Praise vs. Troll/Oppose), and Level 2identifies the target (individual person vs. party). Our approachfine-tunes the Qwen3-VL-2B-Instruct vision-language modelusing parameter-efficient LoRA adapters on task-specific mul-timodal data, with structured output prompting for hierarchi-cal label prediction. We report results for both Tamil andMalayalam subtracks. For Malayalam, our system achievesa Level 1 F1 of 0.9200 and Level 2 F1 of 0.4256 (Avg-F1:0.6728, Rank 5). For Tamil, our system achieves a Level 1 F1of 0.7840 and Level 2 F1 of 0.4885 (Avg-F1: 0.6362, Rank 14).
%U https://aclanthology.org/2026.dravidianlangtech-1.60/
%P 381-386
Markdown (Informal)
[SYNAPSE@DravidianLangTech 2026: Multi-Level Political Meme Classification for Tamil and Malayalam](https://aclanthology.org/2026.dravidianlangtech-1.60/) (KP et al., DravidianLangTech 2026)
ACL