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Toward a conversational model for counsel robots: how different question types elicit different linguistic behaviors

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Abstract

In recent years, robots have been playing the role of counselor or conversational partner in everyday dialogues and interactions with humans. For successful human–robot communication, it is very important to identify the best conversational strategies that can influence the responses of the human client in human–robot interactions. The purpose of the present study is to examine linguistic behaviors in human–human conversation using chatting data to provide the best model for effective conversation in human–robot interaction. We analyzed conversational data by categorizing them into question types, namely Wh-questions and “yes” or “no” (YN) questions, and their correspondent linguistic behaviors (self-disclosure elicitation, self-disclosure, simple “yes” or “no” answers, and acknowledgment). We also compared the utterance length of clients depending on the question type. In terms of linguistic behaviors, the results reveal that the Wh-question type elicited significantly higher rates of self-disclosure elicitation and acknowledgment than YN-questions. Among the Wh-subtype, how was found to promote more linguistic behaviors such as self-disclosure elicitation, self-disclosure, and acknowledgment than other Wh-subtypes. On the other hand, YN-questions generated significantly higher rates of simple “yes” or “no” answers compared to the Wh-question. In addition, Wh-question type elicited longer utterance than the YN-question type. We suggested that the type of questions of the robot counselor must be considered to elicit various linguistic behaviors and utterances of humans. Our research is meaningful in providing efficient conversation strategies for robot utterances that conform to humans’ linguistic behaviors.

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The authors supplied the raw data as supplementary material in an excel file.

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Acknowledgements

This work was supported by the Technology Innovation Program-Industrialized Technology Innovation Project (10077553, Development of Social Robot Intelligence for Social Human-Robot Interaction of Service Robots) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea).

Funding

This work was supported by the Technology Innovation Program-Industrialized Technology Innovation Project (10077553, Development of Social Robot Intelligence for Social Human–Robot Interaction of Service Robots) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea).

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Authors and Affiliations

  1. Department of Communication Disorders, Ewha Womans University, Education Building A-306, 52 Ewhayeodae-gil, Seodaemoon-gu, Seoul, Korea

    Sujin Choi & Jee Eun Sung

  2. The Center for Intelligent and Interactive Robotics, Korea Institute of Science and Technology, Seoul, Korea

    Hanna Lee, Yoonseob Lim & Jongsuk Choi

  3. Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea

    Hanna Lee

  4. Department of HY-KIST Bio-Convergence, Hanyang University, Seoul, Korea

    Yoonseob Lim

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  1. Sujin Choi
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  4. Jongsuk Choi
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  5. Jee Eun Sung
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Contributions

Jee Eun Sung contributed to conceptualization and writing—review and editing and acquired funding; Jee Eun Sung and Yoonseob Lim contributed to methodology; Sujin Choi and Hanna Lee contributed to formal analysis and writing—original draft preparation and helped in investigation; Jee Eun Sung, Yoonseob Lim, and Jongsuk Choi contributed to supervision.

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Correspondence to Jee Eun Sung.

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The Institutional Review Board at the Korea Institute of Science and Technology approved all the experiments used in this study (IRB number: 2018–019).

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Choi, S., Lee, H., Lim, Y. et al. Toward a conversational model for counsel robots: how different question types elicit different linguistic behaviors. Intel Serv Robotics 14, 373–385 (2021). https://doi.org/10.1007/s11370-021-00375-6

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