Modeling and Utilizing User's Internal State in Movie Recommendation Dialogue

TL;DR

This paper develops a dialogue system for movie recommendations that estimates users' internal states—knowledge, interest, and engagement—and adapts responses accordingly, improving naturalness and user experience.

Contribution

It models the user's internal state in dialogues and demonstrates how response adaptation based on this estimation enhances dialogue naturalness.

Findings

High accuracy in UIS estimation achieved

Response adaptation improves dialogue naturalness

Modeling UIS elements benefits dialogue systems

Abstract

Intelligent dialogue systems are expected as a new interface between humans and machines. Such an intelligent dialogue system should estimate the user's internal state (UIS) in dialogues and change its response appropriately according to the estimation result. In this paper, we model the UIS in dialogues, taking movie recommendation dialogues as examples, and construct a dialogue system that changes its response based on the UIS. Based on the dialogue data analysis, we model the UIS as three elements: knowledge, interest, and engagement. We train the UIS estimators on a dialogue corpus with the modeled UIS's annotations. The estimators achieved high estimation accuracy. We also design response change rules that change the system's responses according to each UIS. We confirmed that response changes using the result of the UIS estimators improved the system utterances' naturalness in both dialogue-wise evaluation and utterance-wise evaluation.