Fine-Grained Emotion Recognition via In-Context Learning
This work addresses fine-grained emotion recognition for systems requiring nuanced emotional understanding, representing an incremental improvement by refining existing methods.
The paper tackles the problem of fine-grained emotion recognition by addressing overlooked decision-making processes, proposing Emotion In-Context Learning (EICL) that uses emotionally similar examples and a dynamic soft-label strategy, resulting in significant performance improvements over In-Context Learning on multiple datasets.
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.