Fine-Grained Emotion Recognition via In-Context Learning

ACM International Conference on Information and Knowledge Management (CIKM), pp. 2503–2513

Abstract

Fine-grained emotion recognition aims to identify the emotional type in queries through reasoning and decision-making processes, playing a crucial role in various systems. Recent methods use In-Context Learning (ICL), enhancing the representation of queries in the reasoning process through semantically similar examples, while further improving emotion recognition by explaining the reasoning mechanisms. However, these methods enhance the reasoning process but overlook the decision-making process. This paper investigates decision-making in fine-grained emotion recognition through prototype theory. We show that ICL relies on similarity matching between query representations and emotional prototypes within the model, where emotion-accurate representations are critical. However, semantically similar examples often introduce emotional discrepancies, hindering accurate representations and causing errors. To address this, we propose Emotion In-Context Learning (EICL), which introduces emotionally similar examples and uses a dynamic soft-label strategy to improve query representations in the emotion reasoning process. A two-stage exclusion strategy is then employed to assess similarity from multiple angles, further optimizing the decision-making process. Extensive experiments show that EICL significantly outperforms ICL on multiple datasets.

Authors 6

1. Zhaochun Ren Aachen

   Leiden University

   Affiliation as printed

   Leiden University, Leiden, Netherlands

2. Zhou Yang

   Fuzhou University

   Affiliation as printed

   College of Computer and Data Science, Fuzhou University, Fuzhou, China

3. Chenglong Ye

   Fuzhou University

   Affiliation as printed

   College of Computer and Data Science, Fuzhou University, Fuzhou, China

4. Haizhou Sun
   Affiliation as printed

   SmartMore Company, Shenzhen, China

5. Xiaofei Zhu

   Chongqing University of Technology

   Affiliation as printed

   College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, China

6. Xiangwen Liao

   Fuzhou University

   Affiliation as printed

   College of Computer and Data Science, Fuzhou University, Fuzhou, China