Osaka University, Graduate School of Engineering Science, Toyonaka, 5608531, Japan.
New York University, Department of Sociology, New York, 10003, United States.
Sci Rep. 2024 Nov 28;14(1):29554. doi: 10.1038/s41598-024-81353-7.
This study employs Conversation Analysis to create a recursive model that improves the quality of human-robot interaction. Our research goal is to create a dialogue robot that offers pleasant experiences for users, so they are willing to engage in repeated interactions in daily lives. While there has been dramatic progress in the performance of dialogue robots, there has been less attention to the importance of users' interactional experience compared to the "specs" of the dialogue system. Employing Goffmanian insights and using research in Conversation Analysis (CA), the present study develops a dialogue closing system to exit the interaction. We then experimentally verified that the robot with the dialogue closing system performs better in the user's perception of the robot (i.e. likeability, politeness, and dialogue satisfaction) than the control group. Further, by analyzing the dialogue between the human and the robot through CA, we propose to build a recursive, reflective model to improve the dialogue model design. A constructive approach urges us to reproduce complicated social phenomena in human-robot interaction so that we can investigate the underlying cognitive mechanisms of humans and create robots that can convey human-like cognition functions and coexist with humans. Taking such a constructive approach, we posit that our recursive model for dialogue systems that uses CA insights and then qualitatively analyzes conversational data can enhance the quality of dialogue systems because the model elucidates which properties of a conversation humans need to experience a conversational robot as human-like. Our study suggests that interactional morality - particularly conversational closings - is one property of human interactions that humans likely require social robots to adhere to.
本研究采用会话分析（Conversation Analysis）创建了一个递归模型，以提高人机交互的质量。我们的研究目标是创建一种对话机器人，为用户提供愉悦的体验，使他们愿意在日常生活中进行重复的互动。虽然对话机器人的性能有了显著的提高，但与对话系统的“规格”相比，用户交互体验的重要性却受到了较少的关注。本研究运用戈夫曼（Goffman）的观点，并运用会话分析（CA）的研究成果，开发了一种对话结束系统来结束交互。然后，我们通过实验验证了具有对话结束系统的机器人在用户对机器人的感知（即喜欢度、礼貌度和对话满意度）方面表现优于对照组。此外，通过对人机对话进行 CA 分析，我们提出构建一个递归、反射模型来改进对话模型设计。这种建设性的方法促使我们在人机交互中再现复杂的社会现象，以便研究人类的潜在认知机制，并创造出能够传达类人认知功能并与人类共存的机器人。基于这种建设性的方法，我们假设我们的对话系统递归模型使用 CA 洞察并对会话数据进行定性分析，可以提高对话系统的质量，因为该模型阐明了人类需要在会话机器人中体验到哪些会话属性才能将其视为类人。我们的研究表明，交互道德——特别是会话结束——是人类交互的一个属性，人类可能希望社交机器人遵守。
