Job Interviewer Android with Elaborate Follow-up Question Generation

Autor: Kohei Hara, Shizuka Nakamura, Divesh Lala, Koji Inoue, Katsuya Takanashi, Kenta Yamamoto, Tatsuya Kawahara
Rok vydání: 2020
Předmět:
Zdroj: ICMI
Popis: A job interview is a domain that takes advantage of an android robot's human-like appearance and behaviors. In this work, our goal is to implement a system in which an android plays the role of an interviewer so that users may practice for a real job interview. Our proposed system generates elaborate follow-up questions based on responses from the interviewee. We conducted an interactive experiment to compare the proposed system against a baseline system that asked only fixed-form questions. We found that this system was significantly better than the baseline system with respect to the impression of the interview and the quality of the questions, and that the presence of the android interviewer was enhanced by the follow-up questions. We also found a similar result when using a virtual agent interviewer, except that presence was not enhanced.
Databáze: OpenAIRE