Towards Objective Evaluation of Socially-Situated Conversational Robots: Assessing Human-Likeness through Multimodal User Behaviors

• URL: http://arxiv.org/abs/2308.11020v2
• Date: Mon, 25 Sep 2023 12:10:30 GMT
• Title: Towards Objective Evaluation of Socially-Situated Conversational Robots: Assessing Human-Likeness through Multimodal User Behaviors
• Authors: Koji Inoue, Divesh Lala, Keiko Ochi, Tatsuya Kawahara, Gabriel Skantze
• Abstract summary: This paper focuses on assessing the human-likeness of the robot as the primary evaluation metric. Our approach aims to evaluate the robot's human-likeness based on observable user behaviors indirectly, thus enhancing objectivity and objectivity.
• Score: 26.003947740875482
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper tackles the challenging task of evaluating socially situated conversational robots and presents a novel objective evaluation approach that relies on multimodal user behaviors. In this study, our main focus is on assessing the human-likeness of the robot as the primary evaluation metric. While previous research often relied on subjective evaluations from users, our approach aims to evaluate the robot's human-likeness based on observable user behaviors indirectly, thus enhancing objectivity and reproducibility. To begin, we created an annotated dataset of human-likeness scores, utilizing user behaviors found in an attentive listening dialogue corpus. We then conducted an analysis to determine the correlation between multimodal user behaviors and human-likeness scores, demonstrating the feasibility of our proposed behavior-based evaluation method.

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