用多语言嵌入提升跨语种情绪识别准确率
Team A at SemEval-2025 Task 11: Breaking Language Barriers in Emotion Detection with Multilingual Models
- 采用多语言嵌入+全连接层的架构
- 在六类情绪上实现最佳识别效果
- 适合需要跨语言情绪分析的研究者
本文介绍团队A提交至SemEval 2025任务11《弥合文本情绪检测差距》的系统。该任务要求从文本片段中识别说话者的情绪，每条数据标注六种情绪之一：喜悦、悲伤、恐惧、愤怒、惊讶或厌恶。任务方提供的数据集用于模型训练与评估。实验对比多种方法后，最优结果由多语言嵌入配合全连接层实现。本文详述系统结构，分析实验结果，并强调利用多语言表示对提升文本情绪检测鲁棒性的优势。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。