用大模型融合提升多语言情绪识别效果，性能显著领先。
Large Language Models for Cross-lingual Emotion Detection
- 结合多个大模型及其集成，跨语言理解情绪
- 在WASSA 2024任务中大幅超越其他参赛方案
- 适合对多语言情感分析感兴趣的研究者
本文详细描述了我们在WASSA 2024任务2（跨语言情绪检测）中的参赛系统。我们采用大型语言模型（LLMs）及其集成方法，有效实现不同语言的情绪理解和分类。该方法不仅在性能上大幅优于其他提交方案，还验证了多模型融合对提升表现的有效性。我们对所用各模型的优缺点进行了深入比较，并进行了错误分析，提出了未来改进方向。本工作旨在为情绪检测领域的先进方法提供清晰、全面的理解，使初学者也能快速掌握。
原文摘要 · Abstract (English)
This paper presents a detailed system description of our entry for the WASSA 2024 Task 2, focused on cross-lingual emotion detection. We utilized a combination of large language models (LLMs) and their ensembles to effectively understand and categorize emotions across different languages. Our approach not only outperformed other submissions with a large margin, but also demonstrated the strength of integrating multiple models to enhance performance. Additionally, We conducted a thorough comparison of the benefits and limitations of each model used. An error analysis is included along with suggested areas for future improvement. This paper aims to offer a clear and comprehensive understanding of advanced techniques in emotion detection, making it accessible even to those new to the field.
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