Modeling Multiple Support Strategies within a Single Turn for Emotional Support Conversations

2026-04-20

AI Summary

This work addresses a key limitation in existing empathetic support dialogue systems, which typically assume the use of only a single support strategy per turn and thus fail to capture the nuanced interplay of multiple strategies commonly observed in real-world supportive interactions. To overcome this, the study reframes emotional support modeling as a single-turn, multi-strategy generation task and introduces two novel approaches—All-in-One and One-by-One—augmented with a reinforcement learning–driven cognitive reasoning mechanism that jointly optimizes strategy selection and response generation. Evaluated on the ESConv dataset, the proposed framework demonstrates the feasibility and effectiveness of multi-strategy integration, significantly improving both support quality and dialogue success rates, thereby advancing beyond conventional single-strategy paradigms.

Technology Category

• Natural Language Processing: Conversational AI/Dialog Systems
• Multiagent Systems: Adversarial Agents
• Machine Learning: Multimodal Learning

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Emotional Support Conversation (ESC) aims to assist individuals experiencing distress by generating empathetic and supportive dialogue. While prior work typically assumes that each supporter turn corresponds to a single strategy, real-world supportive communication often involves multiple strategies within a single utterance. In this paper, we revisit the ESC task by formulating it as multi-strategy utterance generation, where each utterance may contain one or more strategy-response pairs. We propose two generation methods: All-in-One, which predicts all strategy-response pairs in a single decoding step, and One-by-One, which iteratively generates strategy-response pairs until completion. Both methods are further enhanced with cognitive reasoning guided by reinforcement learning to improve strategy selection and response composition. We evaluate our models on the ESConv dataset under both utterance-level and dialogue-level settings. Experimental results show that our methods effectively model multi-strategy utterances and lead to improved supportive quality and dialogue success. To our knowledge, this work provides the first systematic empirical evidence that allowing multiple support strategies within a single utterance is both feasible and beneficial for emotional support conversations. All code and data will be publicly available at https://github.com/aliyun/qwen-dianjin.

Problem

• Emotional Support Conversation
• multi-strategy utterance
• support strategy
• dialogue generation
• empathetic response

Innovation

• multi-strategy generation
• emotional support conversation
• reinforcement learning
• cognitive reasoning
• utterance-level modeling