PCQPR: Proactive Conversational Question Planning with Reflection
Shasha Guo, Lizi Liao, Jing Zhang, Cuiping Li, Hong Chen

TL;DR
This paper introduces PCQPR, a proactive question planning method that uses Monte Carlo Tree Search and large language models to steer conversations towards specific conclusions, improving over traditional reactive CQG approaches.
Contribution
It presents a novel conclusion-driven CQG framework with a self-refining planning algorithm that enhances goal-oriented conversational question generation.
Findings
PCQPR outperforms existing CQG methods in goal-oriented tasks.
The approach effectively guides conversations towards desired conclusions.
Self-refinement improves question relevance and strategic planning.
Abstract
Conversational Question Generation (CQG) enhances the interactivity of conversational question-answering systems in fields such as education, customer service, and entertainment. However, traditional CQG, focusing primarily on the immediate context, lacks the conversational foresight necessary to guide conversations toward specified conclusions. This limitation significantly restricts their ability to achieve conclusion-oriented conversational outcomes. In this work, we redefine the CQG task as Conclusion-driven Conversational Question Generation (CCQG) by focusing on proactivity, not merely reacting to the unfolding conversation but actively steering it towards a conclusion-oriented question-answer pair. To address this, we propose a novel approach, called Proactive Conversational Question Planning with self-Refining (PCQPR). Concretely, by integrating a planning algorithm inspired by…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSpeech and dialogue systems
Methodstravel james
