Creating chatbots to behave like real people is important in terms of believability. Errors in general chatbots and chatbots that follow a rough persona have been studied, but those in chatbots that behave like real people have not been thoroughly investigated. We collected a large amount of user interactions of a generation-based chatbot trained from large-scale dialogue data of a specific character, i.e., target person, and analyzed errors related to that person. We found that person-specific errors can be divided into two types: errors in attributes and those in relations, each of which can be divided into two levels: self and other. The correspondence with an existing taxonomy of errors was also investigated, and person-specific errors that should be addressed in the future were clarified.

To develop a dialogue system that can build common ground with users, the process of building common ground through dialogue needs to be clarified. However, the studies on the process of building common ground have not been well conducted; much work has focused on finding the relationship between a dialogue in which users perform a collaborative task and its task performance represented by the final result of the task. In this study, to clarify the process of building common ground, we propose a data collection method for automatically recording the process of building common ground through a dialogue by using the intermediate result of a task. We collected 984 dialogues, and as a result of investigating the process of building common ground, we found that the process can be classified into several typical patterns and that conveying each worker’s understanding through affirmation of a counterpart’s utterances especially contributes to building common ground. In addition, toward dialogue systems that can build common ground, we conducted an automatic estimation of the degree of built common ground and found that its degree can be estimated quite accurately.