Modeling and Utilizing User's Internal State in Movie Recommendation Dialogue
This work addresses the problem of creating more natural and adaptive dialogue systems for users by incorporating estimations of their internal state, which is an incremental improvement for dialogue system developers.
This paper models a user's internal state (UIS) in movie recommendation dialogues as knowledge, interest, and engagement. The authors trained UIS estimators on an annotated dialogue corpus, achieving high estimation accuracy, and designed response rules based on these estimations. This approach improved the naturalness of system utterances in both dialogue-wise and utterance-wise evaluations.
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.