IntentionESC: An Intention-Centered Framework for Enhancing Emotional Support in Dialogue Systems

TL;DR

This paper introduces the IntentionESC framework, which enhances emotional support dialogue systems by inferring supporter intentions through a novel ICECoT mechanism, improving response effectiveness.

Contribution

The paper presents a new intention-centered framework and ICECoT mechanism that enable LLMs to better understand and infer supporter intentions in emotional support conversations.

Findings

ICECoT improves support response quality.

Automated annotation enhances training data quality.

Framework achieves better emotional support efficacy.

Abstract

In emotional support conversations, unclear intentions can lead supporters to employ inappropriate strategies, inadvertently imposing their expectations or solutions on the seeker. Clearly defined intentions are essential for guiding both the supporter's motivations and the overall emotional support process. In this paper, we propose the Intention-centered Emotional Support Conversation (IntentionESC) framework, which defines the possible intentions of supporters in emotional support conversations, identifies key emotional state aspects for inferring these intentions, and maps them to appropriate support strategies. While Large Language Models (LLMs) excel in text generating, they fundamentally operate as probabilistic models trained on extensive datasets, lacking a true understanding of human thought processes and intentions. To address this limitation, we introduce the Intention Centric Chain-of-Thought (ICECoT) mechanism. ICECoT enables LLMs to mimic human reasoning by analyzing emotional states, inferring intentions, and selecting suitable support strategies, thereby generating more effective emotional support responses. To train the model with ICECoT and integrate expert knowledge, we design an automated annotation pipeline that produces high-quality training data. Furthermore, we develop a comprehensive evaluation scheme to assess emotional support efficacy and conduct extensive experiments to validate our framework. Our data and code are available at https://github.com/43zxj/IntentionESC_ICECoT.