Host: The Japanese Society for Artificial Intelligence
Name : 34th Annual Conference, 2020
Number : 34
Location : Online
Date : June 09, 2020 - June 12, 2020
Linguistic alignment refers to the use of similar words to a conversational partner. In this research, we analyzed feature values of linguistic alignment in human-human non-task-oriented dialogue. Based on the obtained feature values, we then verified the influence on users by dialogue robots with linguistic alignment taking users' attributions into account. We conducted a control experiment using Wizard of Oz method for 38 subjects. The experimental conditions were divided into low-frequency, medium-frequency, and high-frequency according to the frequency of linguistic alignment during one dialogue. For each frequency condition, an experimental group (linguistic alignment) and a control group (backchannel) were set up. After each experiment, the subjects were required to answer three questions relating to empathy and desire of continuing dialogue using five-point Likert scale. As a result, the influence on users' empathy and desire of continuing dialogue by dialogue robots with linguistic alignment is investigated. Furthermore, it is suggested that the more negative the user's attitude toward the robot, the higher the effect of linguistic alignment.