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.

近年来，机器人在与人类的日常对话和互动中一直扮演着顾问或对话伙伴的角色。对于成功的人机交流，确定可以影响人类客户在人机交互中的反应的最佳对话策略非常重要。本研究的目的是使用聊天数据检查人与人对话中的语言行为，为人机交互中的有效对话提供最佳模型。我们通过将对话数据分类为问题类型（即 Wh 问题和“是”或“否”（YN）问题）及其对应的语言行为（自我披露引出、自我披露、简单的“是”或“否”）来分析会话数据”的回答，并致谢）。我们还根据问题类型比较了客户的话语长度。在语言行为方面，结果表明，Wh 问题类型引发的自我披露引发和承认率明显高于 YN 问题。在 Wh 亚型中，与其他 Wh 亚型相比，如何发现如何促进更多的语言行为，例如自我披露引发、自我披露和承认。另一方面，与 Wh 问题相比，YN 问题产生了显着更高的简单“是”或“否”答案率。此外，Wh 问题类型比 YN 问题类型引出更长的话语。我们建议必须考虑机器人顾问的问题类型，以引发人类的各种语言行为和话语。我们的研究对于为符合人类语言行为的机器人话语提供有效的对话策略具有重要意义。