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.

通过大型语言模型，我们提出了一种名为 InstructERC 的新方法，将情绪识别对话（ERC）任务从辨别性框架改为生成性框架，通过引入简单而有效的检索模板模块以及两个额外的情绪对齐任务，显式地整合多层次的对话监督信息和隐含的对话角色关系和未来情绪趋势，从而显著提高了性能，在三个常用的 ERC 数据集上达到了全面的 SOTA 水平。