Team A at SemEval-2025 Task 11: Breaking Language Barriers in Emotion Detection with Multilingual Models
This work addresses emotion detection for text analysis, but it appears incremental as it applies existing methods to a new dataset from a shared task.
The paper tackled emotion detection from text by identifying six emotions in text snippets, achieving the best performance with multilingual embeddings and a fully connected layer, though no concrete numbers were provided in the abstract.
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 embeddings 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.