S⌃4: Operationalizing Speech Act Theory for Strategic Semi-Structured Psychiatric Interview
Guanqun Bi, Zhoufu Liu, Zhuang Chen, Dazhen Wan, Xiyao Xiao, Minlie Huang
Abstract
Psychiatric interviewing is a strategic, goaloriented interaction that requires proactively steering the conversation to elicit latent information. However, existing methods often degenerate into rigid interrogation or aimless chitchat due to a lack of strategic planning. In this work, we introduce S 4 , a comprehensive framework grounded in Speech Act Theory, modeling the interview as a unified process of internal strategy (Illocution and Perlocution) and external realization (Locution). We synthesize a large-scale dataset with fine-grained psychiatric speech act annotations. Trained on this data, S 4 employs reinforcement learning driven by long-term therapeutic effects to optimize the strategic chaining of atomic acts, aiming to maximally elicit information and maintain patient engagement. Experiments demonstrate that S 4 significantly outperforms baselines, validating the effectiveness of our effectdriven strategic modeling. Action (A) Sample Locution (L) Definition & Intended Perlocution (P) I. Information Seeking (Directives: Eliciting Disclosure) Explore "How have you been sleeping?" Solicit Narrative: Ask open-ended questions to elicit detailed disclosure and expand symptom scope. Probe "Could you tell me more about that?" Deepen Inquiry: Follow up on ambiguity to clarify details and deepen focus. Confirm "Do you feel this way every day?" Pinpoint Fact: Ask closed-ended questions to verify diagnostic criteria. Clarify "By 'fatigue', I mean tiredness." Resolve Confusion: Provide explanations to align cognition and correct misunderstandings. II. Affective Regulation (Expressives: Modifying State) Validate "That sounds incredibly hard." Affirm Emotion: Acknowledge patient distress to lower defensiveness and build trust. Support "I understand. Please go on." Maintain Flow: Use back-channeling to demonstrate active listening and boost efficacy. Ease "Do you have any hobbies?" Reduce Tension: Engage in non-clinical conversation to de-escalate anxiety and humanize the agent. III. Interview Management (Representatives: Setting Frame) Initiate "Hi, I'm your AI counselor." Set Frame: Establish professional boundaries and the purpose of the session. Conclude "Thanks for sharing. Take care." Ensure Closure: Formally end the session to provide a safe exit and consolidation.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext f4024d41-5507-40a9-a622-c36abe6fbd9cBuilds on2
- D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented ChatBinwei Yao, Chao Shi, Likai Zou, Lingfeng Dai et al.EMNLP 2022 · 23 citations
- From Medical Records to Diagnostic Dialogues: A Clinical-Grounded Approach and Dataset for Psychiatric ComorbidityTianxi Wan, Jiaming Luo, Siyuan Chen, Kunyao Lan et al.ICLR 2026 · 3 citations
Related papers
- Toward Flexible Psychiatric History-Taking and Visualization: Exploring Clinician Perspectives with Large Language ModelsYugyeong Jung, Thu Hoang Anh Vo, Hyun Seung Moon, Jae Young Choi et al.CHI 2026 · 1 citation
- Facilitating Multi-turn Emotional Support Conversation with Positive Emotion Elicitation: A Reinforcement Learning ApproachJinfeng Zhou, Zhuang Chen, Bo Wang, Minlie HuangACL 2023 · 17 citations
- PsyAdvisor: A Plug-and-Play Strategy Advice Planner with Proactive Questioning in Psychological ConversationsYuxin Hu, Danni Liu, Bo Liu, Yida Chen et al.ACL 2025
- ProMed: Shapley Information Gain Guided Reinforcement Learning for Proactive Medical LLMsHongxin Ding, Baixiang Huang, Yue Fang, Weibin Liao et al.ACL 2026 · 10 citations
- Scaffolded Turns and Logical Conversations: Designing Humanized LLM-Powered Conversational Agents for Hospital Admission InterviewsDingdong Liu, Yujing Zhang, Bolin Zhao, Shuai Ma et al.CHI 2025 · 22 citations
