Dialogue Management by Estimating User’s Internal State Using the Movie Recommendation Dialogue

Abstract

Intelligent dialogue systems are expected to be a new interface between humans and machines. An ideal intelligent dialogue system should estimate the user’s internal states and incorporate the estimation results into its response appropriately. In this paper, we focus on the movie recommendation dialogues and propose a dialogue system that considers the user’s internal state. First, we build a movie recommendation dialogue system and collect dialogue data. Based on the analysis of the collected dialogue data, we model and annotate the user’s internal states in three aspects: knowledge, interest, and engagement. Second, we train the user’s internal state estimators on the dialogue corpus with the annotations of the user’s internal states. The trained estimator achieved high accuracy on the annotated corpus. Further, we design a set of rules that modify the system’s responses according to each user’s internal state. We confirmed that the response modifications based on the results of the user’s internal state estimator improve the naturalness of the system utterances in both dialogue evaluation and utterance evaluation.