The development of emotion recognition in dialogue (ERC) has been consistently hindered by the complexity of pipeline designs, leading to ERC models that often overfit to specific datasets and dialogue patterns. In this study, we propose a novel approach, namely InstructERC, to reformulates the ERC task from a discriminative framework to a generative framework based on Large Language Models (LLMs) . InstructERC has two significant contributions: Firstly, InstructERC introduces a simple yet effective retrieval template module, which helps the model explicitly integrate multi-granularity dialogue supervision information by concatenating the historical dialog content, label statement, and emotional domain demonstrations with high semantic similarity. Furthermore, we introduce two additional emotion alignment tasks, namely speaker identification and emotion prediction tasks, to implicitly model the dialogue role relationships and future emotional tendencies in conversations. Our LLM-based plug-and-play plugin framework significantly outperforms all previous models and achieves comprehensive SOTA on three commonly used ERC datasets. Extensive analysis of parameter-efficient and data-scaling experiments provide empirical guidance for applying InstructERC in practical scenarios. Our code will be released after blind review.

翻译：对话情感识别（ERC）的发展长期受限于流水线设计的复杂性，导致现有ERC模型常对特定数据集与对话模式产生过拟合。本研究提出一种名为InstructERC的创新方法，将ERC任务从判别式框架重构为基于大语言模型（LLMs）的生成式框架。InstructERC具有两大核心贡献：首先，通过构建简洁高效的检索模板模块，将历史对话内容、标签陈述及高语义相似度的情感领域示例进行拼接，帮助模型显式整合多粒度对话监督信息；其次，引入说话人识别与情感预测两项额外情感对齐任务，隐式建模对话中的角色关系与未来情感倾向。基于LLM的即插即用插件框架在三个常用ERC数据集上全面超越现有模型，取得最优性能。参数高效与数据规模扩展实验的深入分析为InstructERC在真实场景中的部署提供了实证指导。相关代码将在盲审后开源。