Team A at SemEval-2025 Task 11: Breaking Language Barriers in Emotion Detection with Multilingual Models

This paper describes the system submitted by Team A to SemEval 2025 Task 11, "Bridging the Gap in Text-Based Emotion Detection. " The task involved identifying the perceived emotion of a speaker from text snippets, with each instance annotated with one of six emotions: joy, sadness, fear, anger, surprise, or disgust. A dataset provided by the task organizers served as the foundation for training and evaluating our models. Among the various approaches explored, the best performance was achieved using multilingual em-beddings combined with a fully connected layer. This paper details the system architecture, discusses experimental results, and highlights the advantages of leveraging multilingual representations for robust emotion detection in text.