Hate speech poses a significant challenge on social networks, particularly in Devanagari scripted languages, where subtle expressions can lead to harmful narratives. This paper details our participation in the “Shared Task on Natural Language Understanding of Devanagari Script Languages” at CHIPSAL@COLING 2025, addressing hate speech detection and target identification. In Sub-task B, we focused on classifying the text either hate or non-hate classified text to determine the presence of hate speech, while Sub-task C focused on identifying targets, such as individuals, organizations, or communities. We utilized the XLM-RoBERTa model as our base and explored various adaptations, including Adaptive Weighting and Gated Adaptive Weighting methods. Our results demonstrated that the Hierarchical Gated adaptive weighting model achieved 86% accuracy in hate speech detection with a macro F1 score of 0.72, particularly improving performance for minority class detection. For target detection, the same model achieved 75% accuracy and a 0.69 macro F1 score. Our proposed architecture demonstrated competitive performance, ranking 8th in Subtask B and 11th in Subtask C among all participants.

Misogyny memes pose a significant challenge on social networks, particularly in Dravidian-scripted languages, where subtle expressions can propagate harmful narratives against women. This paper presents our approach for the “Shared Task on MisogynyMeme Detection,” organized as part of DravidianLangTech@NAACL 2025, focusing on misogyny meme detection in Tamil andMalayalam. To tackle this problem, we proposed a multi-model framework that integrates three distinct models: M1 (ResNet-50 + google/muril-large-cased), M2 (openai/clipvit- base-patch32 + ai4bharat/indic-bert), and M3 (ResNet-50 + ai4bharat/indic-bert). Thefinal classification is determined using a majority voting mechanism, ensuring robustness by leveraging the complementary strengths ofthese models. This approach enhances classification performance by reducing biases and improving generalization. Our model achievedan F1 score of 0.77 for Tamil, significantly improving misogyny detection in the language. For Malayalam, the framework achieved anF1 score of 0.84, demonstrating strong performance. Overall, our method ranked 5th in Tamil and 4th in Malayalam, highlighting itscompetitive effectiveness in misogyny meme detection.