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 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.

• Publication year

  2025

• Venue

  arXiv.org

• Publication date

  2025-02-27

• Fields of study

  Linguistics, Computer Science

• Identifiers
  DOI 10.48550/arXiv.2502.19856arXiv 2502.19856
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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