This paper explores multilingual emotion classification across binary classification, intensity estimation, and cross-lingual detection tasks. To address linguistic variability and limited annotated data, we evaluate various deep learning approaches, including transformer-based embeddings and traditional classifiers. After extensive experimentation, language-specific embedding models were selected as the final approach, given their superior ability to capture linguistic and cultural nuances. Experiments on high- and low-resource languages demonstrate that this method significantly improves performance, achieving competitive macro-average F1 scores. Notably, in languages such as Tigrinya and Kinyarwanda for cross-lingual detection task, our approach achieved a second-place ranking, driven by the incorporation of advanced preprocessing techniques. Despite these advances, challenges remain due to limited annotated data in underrepresented languages and the complexity of nuanced emotional expressions. The study highlights the need for robust, language-aware emotion recognition systems and emphasizes future directions, including expanding multilingual datasets and refining models.

Visual Word Sense Disambiguation (VWSD) task aims to find the most related image among 10 images to an ambiguous word in some limited textual context. In this work, we use AltCLIP features and a 3-layer standard transformer encoder to compare the cosine similarity between the given phrase and different images. Also, we improve our model’s generalization by using a subset of LAION-5B. The best official baseline achieves 37.20% and 54.39% macro-averaged hit rate and MRR (Mean Reciprocal Rank) respectively. Our best configuration reaches 39.61% and 56.78% macro-averaged hit rate and MRR respectively. The code will be made publicly available on GitHub.