Speakers build rapport in the process of aligning conversational behaviors with each other. Rapport engendered with a teachable agent while instructing domain material has been shown to promote learning. Past work on lexical alignment in the field of education suffers from limitations in both the measures used to quantify alignment and the types of interactions in which alignment with agents has been studied. In this paper, we apply alignment measures based on a data-driven notion of shared expressions (possibly composed of multiple words) and compare alignment in one-on-one human-robot (H-R) interactions with the H-R portions of collaborative human-human-robot (H-H-R) interactions. We find that students in the H-R setting align with a teachable robot more than in the H-H-R setting and that the relationship between lexical alignment and rapport is more complex than what is predicted by previous theoretical and empirical work.
翻译:发言者在相互调和对话行为的过程中建立起了和谐关系。在指导领域材料的同时,与可教学的代理人产生了和谐关系,这证明促进了学习。过去在教育领域进行词汇调整的工作在量化一致性的措施和研究与代理关系协调的相互作用类型方面都受到限制。在本文中,我们根据数据驱动的共享表达方式概念(可能由多个词组成)采取调整措施,并将人类-机器人(H-H-R)的一对一互动与人类-机器人(H-H-H-R)协作互动的H-R部分进行对比。我们发现,H-R的学生与可教学的机器人的组合比H-H-R环境中的组合要多,而且词汇调整和连接之间的关系比先前理论和实验工作预测的要复杂得多。