Yoshinori Maeda


2024

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Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning
Zhouhang Xie | Bodhisattwa Prasad Majumder | Mengjie Zhao | Yoshinori Maeda | Keiichi Yamada | Hiromi Wakaki | Julian McAuley
Findings of the Association for Computational Linguistics: ACL 2024

We consider the task of building a dialogue system that can motivate users to adopt positive lifestyle changes, Motivational Interviewing (MI). Addressing such a task requires a system that could infer how to motivate the user effectively. We propose DIIR, a framework that is capable of learning and applying conversation strategies in the form of natural language inductive rules from expert demonstrations. Automatic and human evaluation on instruction-following large language models show natural language strategies descriptions discovered by DIIR can improve active listening skills, reduce unsolicited advice, and promote more collaborative and less authoritative conversations, outperforming in-context demonstrations that are over 50 times longer.