@inproceedings{bi-etal-2026-4,
title = "S{\textasciicircum}4: Operationalizing Speech Act Theory for Strategic Semi-Structured Psychiatric Interview",
author = "Bi, Guanqun and
Liu, Zhoufu and
Chen, Zhuang and
Wan, Dazhen and
Xiao, Xiyao and
Huang, Minlie",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1802/",
pages = "38881--38899",
ISBN = "979-8-89176-390-6",
abstract = "Psychiatric interviewing is a strategic, goal-oriented 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 S4, 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, S4Dial 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 S4 significantly outperforms baselines, validating the effectiveness of our effect-driven strategic modeling."
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%0 Conference Proceedings
%T S⌃4: Operationalizing Speech Act Theory for Strategic Semi-Structured Psychiatric Interview
%A Bi, Guanqun
%A Liu, Zhoufu
%A Chen, Zhuang
%A Wan, Dazhen
%A Xiao, Xiyao
%A Huang, Minlie
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F bi-etal-2026-4
%X Psychiatric interviewing is a strategic, goal-oriented 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 S4, 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, S4Dial 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 S4 significantly outperforms baselines, validating the effectiveness of our effect-driven strategic modeling.
%U https://aclanthology.org/2026.acl-long.1802/
%P 38881-38899
Markdown (Informal)
[S^4: Operationalizing Speech Act Theory for Strategic Semi-Structured Psychiatric Interview](https://aclanthology.org/2026.acl-long.1802/) (Bi et al., ACL 2026)
ACL