InstructERC: Reforming Emotion Recognition in Conversation with Multi-task Retrieval-Augmented Large Language Models

TL;DR

InstructERC transforms emotion recognition in conversation from a discriminative to a generative approach using large language models, integrating multi-granularity supervision, role modeling, and unified emotion labels to achieve state-of-the-art results.

Contribution

It introduces a novel generative framework for ERC with retrieval templates, additional emotion alignment tasks, and unified emotion labels across benchmarks.

Findings

Achieves state-of-the-art performance on three ERC datasets.

Outperforms previous models significantly.

Provides empirical insights for practical application.

Abstract

The field of emotion recognition of conversation (ERC) has been focusing on separating sentence feature encoding and context modeling, lacking exploration in generative paradigms based on unified designs. In this study, we propose a novel approach, InstructERC, to reformulate the ERC task from a discriminative framework to a generative framework based on Large Language Models (LLMs). InstructERC makes three significant contributions: (1) it introduces a simple yet effective retrieval template module, which helps the model explicitly integrate multi-granularity dialogue supervision information. (2) 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. (3) Pioneeringly, we unify emotion labels across benchmarks through the feeling wheel to fit real application scenarios. InstructERC still perform impressively on this unified dataset. Our LLM-based 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 provides empirical guidance for applying it in practical scenarios.