团队A在SemEval-2025任务11中：用多语言模型打破情感检测的语言障碍
Team A at SemEval-2025 Task 11: Breaking Language Barriers in Emotion Detection with Multilingual Models
AI总结：
团队A在SemEval-2025任务11中，采用多语言嵌入结合全连接层的方法，在六类情感检测任务上取得最佳性能，验证了多语言表示的有效性。
摘要：
本文描述了团队A提交给SemEval 2025任务11“弥合基于文本的情感检测差距”的系统。该任务涉及从文本片段中识别说话者的感知情感，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作为训练和评估我们模型的基础。在探索的各种方法中，最佳性能是通过多语言嵌入结合全连接层实现的。本文详细介绍了系统架构，讨论了实验结果，并强调了利用多语言表示进行稳健文本情感检测的优势。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
