Work

Schema-Guided Response Generation using Multi-Frame Dialogue State for Motivational Interviewing Systems

Year: 2026

Type: conference-paper

Abstract: The primary goal of Motivational Interviewing (MI) is to help clients build their own motivation for behavioral change.To support this in dialogue systems, it is essential to guide large language models (LLMs) to generate counselor responses aligned with MI principles.By employing a schema-guided approach, this study proposes a method for updating multi-frame dialogue states and a strategy decision mechanism that dynamically determines the response focus in a manner grounded in MI principles.The proposed method was implemented in a dialogue system on two different datasets and evaluated through a user study.Results showed that the proposed method successfully generates responses aligned with MI principle and frequently asks questions to elicit change talk 1 .

Source: Findings of the Association for Computational Linguistics: ACL (Findings of ACL)

DOI: 10.18653/v1/2026.findings-acl.2063

Authors: Jie Zeng, Yukiko Nakano

Institution: Seikei University

Language: English

Topic: Speech and dialogue systems

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Japan Society for the Promotion of Science, Core Research for Evolutional Science and Technology

Award: Modeling and Supporting Group Communication by Considering Neurodiversity