# 单轮多策略情感支持对话建模

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-04-20 08:00
- AIHOT 链接：https://aihot.virxact.com/items/cmo82l6vp03ldslmlngpx7u0c
- 原文链接：https://arxiv.org/abs/2604.17972

## AI 摘要

现有情感支持对话系统通常假设每轮仅使用单一策略，但现实中单轮常包含多种支持策略。本研究将ESC任务重新定义为多策略话语生成，提出All-in-One和One-by-One两种方法，分别通过单步解码和迭代方式生成策略-回复对，并引入强化学习引导的认知推理优化策略选择。在ESConv数据集上的实验表明，该方法能有效建模多策略话语，显著提升支持质量与对话成功率，首次系统验证了单轮多策略的可行性和有效性。

## 正文

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.