首页|Dynamic Strategy Prompt Reasoning for Emotional Support Conversation

Dynamic Strategy Prompt Reasoning for Emotional Support Conversation

扫码查看
原文链接
  • NETL
  • NSTL
  • IEEE
An emotional support conversation (ESC) system aims to reduce users' emotional distress by engaging in conversation using various reply strategies as guidance. To develop instructive reply strategies for an ESC system, it is essential to consider the dynamic transitions of users' emotional states through the conversational turns. However, existing methods for strategy-guided ESC systems struggle to capture these transitions as they overlook the inference of fine-grained user intentions. This oversight poses a significant obstacle, impeding the model's ability to derive pertinent strategy information and, consequently, hindering its capacity to generate emotionally supportive responses. To tackle this limitation, we propose a novel dynamic strategy prompt reasoning model (DSR), which leverages sparse context relation deduction to acquire adaptive representation of reply strategies as prompts for guiding the response generation process. Specifically, we first perform turn-level commonsense reasoning with different approaches to extract auxiliary knowledge, which enhances the comprehension of user intention. Then we design a context relation deduction module to dynamically integrate interdependent dialogue information, capturing granular user intentions and generating effective strategy prompts. Finally, we utilize the strategy prompts to guide the generation of more relevant and supportive responses. DSR model is validated through extensive experiments conducted on a benchmark dataset, demonstrating its superior performance compared to the latest competitive methods in the field.

Emotion recognitionOral communicationCommonsense reasoningHistoryInformation processingGeneratorsComputersComputer scienceVisualizationSemantics

Yiting Liu、Liang Li、Yunbin Tu、Beichen Zhang、Zheng-Jun Zha、Qingming Huang

展开 >

Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China|School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China

Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China

School of Information Science and Technology, University of Science and Technology of China, Hefei, China

School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China|Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

展开 >

2025

10.1109/TMM.2024.3521669

IEEE transactions on multimedia

IEEE transactions on multimedia

ISSN：
年,卷(期)：2025.27(1)
  • 70