Vaccine-critical memes have emerged as a growing challenge for public health communication, combining images and text to spread misinformation in ways that are difficult to detect automatically. In this paper, we have described our system for the EEUCA 2026 Shared Task on Multimodal Vaccine-Critical Meme Detection, classifying memes from the VaxMeme dataset into Vaccine-Critical, Neutral and Pro-Vaccine categories. We have experimented with multiple text encoders and visual backbones, finding that Twitter-RoBERTa fused with CLIP ViT-L/14 through gated cross-modal attention has achieved a test macro F1 of 0.8357. We have further shown that domain-specific pretraining has outperformed larger general-purpose models, highlighting the importance of domain adaptation over raw model scale. Finally, our system has secured the 3rd position on the shared task leaderboard.

Hope speech has played a vital role in online communities, yet most NLP work has focused on English and a few high-resource languages, leaving code-mixed varieties like Tulu largely unexplored. In the Shared Task on Hope Speech Detection in Code-Mixed Tulu at DravidianLangTech@ACL 2026, we have tackled two subtasks: (i) coarse-grained classification into Encouraging, Discouraging, Uninvolved and Blended categories (Task 1) and (ii) fine-grained classification into Optimistic, Realistic, Inspiring, Fading and Hopelessness (Task 2).We have fine-tuned three multilingual transformer encoders XLM-RoBERTa-base, MuRIL and mBERT on the official training splits. In Task 1, a three-way soft-voting ensemble of all three models has yielded the best performance with a macro F1 of 0.58, securing 1st place. In Task 2, XLM-RoBERTa-base alone has outperformed both MuRIL and mBERT, achieving a macro F1 of 0.42 and also securing 1st place.

Abusive language targeting women has been a serious problem on Tamil social media and building systems to detect it automatically is harder than it looks. Tamil is morphologically complex, people have written it mixed with English in ways no dictionary has accounted for and a lot of the hostility has been indirect enough that has slipped past models trained on surface patterns. In the Shared Task on Abusive Tamil Text Targeting Women on Social Media DravidianLangTech@ACL 2026, we have worked on classifying Tamil YouTube comments as Abusive or Non-Abusive. We have trained three transformer models four times each with different learning rates, giving us 12 models total. Their predicted probabilities have been averaged to make the final decision. The 12-model ensemble has achieved a macro F1 of 0.8086, outperforming all individual models and securing 4th place in the shared task. Combining Tamil-specialized and multilingual transformer models has outperformed any single-architecture approach.