Hope speech detection appears to have an essential role to play in fostering positive and inclusive communication on social media, especially in low-resource multilingual settings. This paper describes the system submitted by Team Oryu for Task 1: Coarse-Grained Hope Tone Classification in Code-Mixed Tulu. The task involves classifying comments in social media texts into one of the four classes: Encouraging, Discouraging, Uninvolved, and Blended Tone. The texts in this task show heavy code-mixing between Tulu, English, and Kannada. In order to overcome this challenge, we employed a fine-tuned multilingual transformer model, code-mixed text processing, data augmentation, and class-weighted loss to handle class imbalance. Our proposed system achieved a Macro F1-score of 63%, securing 3rd position on the shared task. The results demonstrate the efficacy of multilingual transformer models in emotionally nuanced classification in code-mixed environments while underscoring the difficulties in capturing blended emotional tones.

With the proliferation of multimodal content on various social media platforms, automated hate speech detection has emerged as a challenge, especially in meme-based communication, where meaning arises from interactions between text and images. In these situations, unimodal techniques are inadequate in capturing semantics. In order to address such issues, a late-fusion-based multimodal hate speech detection framework has been proposed and implemented for the CHiPSAL shared task. In the proposed framework, multimodal content is processed by utilizing XLM-RoBERTa for multilingual text representation and a Vision Transformer (ViT) for visual representation. Both modal representations are fused using a fully connected classification head and are used for binary hate speech detection. The findings suggest that multimodal content effectively captures features from individual modalities and helps improve hate speech detection accuracy by obtaining a Macro F1-score of 0.66 and ranking 5th on the leaderboard. Also, transformer-based multimodal fusion performs effectively and acts as a reliable baseline for hate speech detection in low-resource multilingual meme-based communication scenarios.