This paper presents our system submission to the Shared Task on Hope Speech Detection in Code-Mixed Tulu Language at DravidianLangTech @ ACL 2026. We introduce a transformer-based approach built on XLM RoBERTa-base for multilingual hope speechclassification. Our system addresses two sub tasks: coarse-grained classification of hope versus non-hope speech and fine-grained categorization of different hope expressions. Since hope is often expressed in subtle ways, especially in mixed-language text, our model looks at the full context of a sentence to understand its real meaning rather than just focusing on specific words. Experimental results demonstrate that multilingual transformer models effectively model supportive and encouraging expressions, underscoring their suitability for promoting constructive discourse in low-resourceand code-mixed language settings.

This paper presents our approach to the Shared Task on Detecting AI-Generated Product Reviews in Dravidian Languages as part of DravidianLangTech@NAACL 2025. The task focuses on distinguishing between human-written and AI-generated reviews in Tamil and Malayalam, languages rich in linguistic complexities. Using the provided datasets, we implemented machine learning and deep learning models, including Logistic Regression (LR), Support Vector Machine (SVM), and BERT. Through preprocessing techniques like tokenization and TF-IDF vectorization, we achieved competitive results, with our SVM and BERT models demonstrating superior performance in Tamil and Malayalam respectively. Our findings underscore the unique challenges of working with Dravidian languages in this domain and highlight the importance of robust feature extraction.