E-ICL: Enhancing Fine-Grained Emotion Recognition through the Lens of Prototype Theory

TL;DR

This paper introduces E-ICL, a method that improves fine-grained emotion recognition by addressing prototype theory limitations in in-context learning, using emotionally accurate prototypes and exclusion strategies without extra training.

Contribution

It proposes E-ICL, a novel approach that enhances emotion recognition accuracy by leveraging emotionally relevant prototypes and exclusionary strategies, with a plug-and-play auxiliary model.

Findings

E-ICL outperforms baseline models on multiple emotion datasets.

E-ICL boosts LLM performance by over 4% even with low-quality auxiliary models.

E-ICL effectively addresses prototype-related deficiencies in emotion recognition.

Abstract

In-context learning (ICL) achieves remarkable performance in various domains such as knowledge acquisition, commonsense reasoning, and semantic understanding. However, its performance significantly deteriorates for emotion detection tasks, especially fine-grained emotion recognition. The underlying reasons for this remain unclear. In this paper, we identify the reasons behind ICL's poor performance from the perspective of prototype theory and propose a method to address this issue. Specifically, we conduct extensive pilot experiments and find that ICL conforms to the prototype theory on fine-grained emotion recognition. Based on this theory, we uncover the following deficiencies in ICL: (1) It relies on prototypes (example-label pairs) that are semantically similar but emotionally inaccurate to predict emotions. (2) It is prone to interference from irrelevant categories, affecting the accuracy and robustness of the predictions. To address these issues, we propose an Emotion Context Learning method (E-ICL) on fine-grained emotion recognition. E-ICL relies on more emotionally accurate prototypes to predict categories by referring to emotionally similar examples with dynamic labels. Simultaneously, E-ICL employs an exclusionary emotion prediction strategy to avoid interference from irrelevant categories, thereby increasing its accuracy and robustness. Note that the entire process is accomplished with the assistance of a plug-and-play emotion auxiliary model, without additional training. Experiments on the fine-grained emotion datasets EDOS, Empathetic-Dialogues, EmpatheticIntent, and GoEmotions show that E-ICL achieves superior emotion prediction performance. Furthermore, even when the emotion auxiliary model used is lower than 10% of the LLMs, E-ICL can still boost the performance of LLMs by over 4% on multiple datasets.