Unlike empathetic dialogues, the system in emotional support conversations (ESC) is expected to not only convey empathy for comforting the help-seeker, but also proactively assist in exploring and addressing their problems during the conversation. In this work, we study the problem of mixed-initiative ESC where the user and system can both take the initiative in leading the conversation. Specifically, we conduct a novel analysis on mixed-initiative ESC systems with a tailor-designed schema that divides utterances into different types with speaker roles and initiative types. Four emotional support metrics are proposed to evaluate the mixed-initiative interactions. The analysis reveals the necessity and challenges of building mixed-initiative ESC systems. In the light of this, we propose a knowledge-enhanced mixed-initiative framework (KEMI) for ESC, which retrieves actual case knowledge from a large-scale mental health knowledge graph for generating mixed-initiative responses. Experimental results on two ESC datasets show the superiority of KEMI in both content-preserving evaluation and mixed initiative related analyses.

翻译：与共情对话不同，情感支持对话（ESC）系统不仅需要传达共情以安慰求助者，还需在对话过程中主动协助探索并解决其问题。本研究探讨了用户和系统均可主导对话的混合主动ESC问题。具体而言，我们通过定制化模式对混合主动ESC系统进行了新颖分析，该模式将对话语句按说话者角色和主动类型划分为不同类别，并提出四项情感支持指标以评估混合主动交互。分析揭示了构建混合主动ESC系统的必要性与挑战。基于此，我们提出了一种知识增强的混合主动框架（KEMI）用于ESC，该框架从大规模心理健康知识图谱中检索实际案例知识，以生成混合主动式回复。在两个ESC数据集上的实验结果表明，KEMI在内容保留评估和混合主动相关分析中均展现出优越性。