在单轮中建模多种支持策略用于情感支持对话
Modeling Multiple Support Strategies within a Single Turn for Emotional Support Conversations
AI总结：
本文提出在单轮中生成多策略回应的方法，通过All-in-One和One-by-One生成策略，结合强化学习提升策略选择和回应生成，实验证明多策略生成能提高情感支持质量。
摘要：
情感支持对话（ESC）旨在通过生成共情和支持性的对话帮助经历困扰的个体。尽管先前工作通常假设每个支持者回应对应单一策略，现实中的支持交流往往在单个回应中包含多种策略。本文将ESC任务重新表述为多策略回应生成，每个回应可能包含一个或多个策略-回应对。我们提出两种生成方法：All-in-One，一次解码步预测所有策略-回应对；One-by-One，迭代生成策略-回应对直至完成。两种方法进一步通过强化学习引导的认知推理增强策略选择和回应生成。我们在ESConv数据集上评估模型，采用句子级和对话级设置。实验结果表明，我们的方法有效建模多策略回应，提升支持质量和对话成功率。到目前为止，本文提供了首次系统实验证据，证明在单个回应中允许多种支持策略是可行且有益于情感支持对话的。所有代码和数据将在https://github.com/aliyun/qwen-dianjin公开。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
