Female-Type Android’s Drive to Quickly Understand a User’s Concept of Preferences Stimulates Dialogue Satisfaction: Dialogue Strategies for Modeling User’s Concept of Preferences

Abstract

This research develops a conversational robot that stimulates users’ dialogue satisfaction and motivation in non-task-oriented dialogues that include opinion and/or preference exchanges. One way to improve user satisfaction and motivation is by demonstrating the robot’s ability to understand user opinions. In this paper, we explore a method that efficiently obtains the concept of user preferences: likes and dislikes. The concept is acquired by complementing a small amount of user preference data observed in dialogues. As a method for efficient collection, we propose a dialogue strategy that creates utterances with the largest expected complementation. Our experimental results with a female-type android robot suggest that the proposed strategy efficiently obtained user preferences and enhanced dialogue satisfaction. In addition, the strength of user motivation (i.e., long-term willingness to communicate with the android) is only positively correlated with the android’s willingness to understand. Our results not only show the effectiveness of our proposed strategy but also suggest a design theory for dialogue robots to stimulate dialogue motivation, although the current results are derived only from a female-type android.

A Dialogue System that Models User's Opinion Based on Information Content

“…Therefore, we adopted it as a knowledge model that satisfies the requirements of both language generation and knowledge processing. In the previous study [9], it is difficult to increase the number of noun types. On the other hand, in our method using semantic networks, the number of noun types can be increased simply by creating new nodes and adding them to the existing semantic network.…”

“…However, the opinions are limited to the same topic. In the previous study [9], it is not suggested that this method is effective even in the case of mixed topics, such as "pizza" and "cake" where it is not certain that the tendencies of opinion are the same. Hence, This study adopts semantic networks to the model user's opinion.…”

“…However, these do not take into account interactive methods that require additional information input from the user to maximize estimation. Uchida et al [9] have reported a dialogue system that expresses user's opinions as tables and estimates their preferences and similarities of items. The dialogue system asks questions to the user, resulting in an efficient estimation of other user's opinions from a small number of opinion data.…”

“…It is shown that the dialogue system's willingness to understand the user dialogue contributes to the dialogue motivation in a certain topic [9]. However, the relationship between estimation methods and the willingness has only been investigated to a limited extent.…”

“…Understanding (modeling) the user is also important for dialogue systems. Uchida et al [9] have reported that a female android's willingness to understand their user correlates with their satisfaction with the dialogue and their dialogue motivation with it. Another system collects the information necessary for product recommendation through dialogue [10].…”