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.

翻译：本文介绍了 Team A 为 SemEval 2025 任务 11 “弥合基于文本的情感检测的差距” 所提交的系统。该任务旨在从文本片段中识别说话者被感知的情感，每个实例被标注为六种情感之一：喜悦、悲伤、恐惧、愤怒、惊讶或厌恶。任务组织者提供的数据集是我们模型训练和评估的基础。在探索的各种方法中，最佳性能是通过使用多语言嵌入结合全连接层实现的。本文详细阐述了系统架构，讨论了实验结果，并强调了利用多语言表征进行鲁棒的文本情感检测的优势。