Developing mobile robots that can provide guidance with high hospitality remains challenging, as it requires the coordination of spoken interaction, physical navigation, and user engagement. To gain insights that contribute to the development of such robots, we conducted a Wizard-of-Oz (WOZ) study using Teleco, a teleoperated humanoid robot, to explore the factors influencing hospitality in mobile robot guidance. Specifically, we enrolled 30 participants as visitors and two trained operators, who teleoperated the Teleco robot to provide mobile guidance to the participants. A total of 120 dialogue sessions were collected, along with evaluations from both the participants and the operators regarding the hospitality of each interaction. To identify the factors that influence hospitality in mobile guidance, we analyzed the collected dialogues from two perspectives: linguistic usage and multimodal robot behaviors. We first clustered system utterances and analyzed the frequency of categories in high- and low-satisfaction dialogues. The results showed that short responses appeared more frequently in high-satisfaction dialogues. Moreover, we observed a general increase in participant satisfaction over successive sessions, along with shifts in linguistic usage, suggesting a mutual adaptation effect between operators and participants. We also conducted a time-series analysis of multimodal robot behaviors to explore behavioral patterns potentially linked to hospitable interactions.

In dialogue systems, one option for creating a better dialogue experience for the user is to have a human operator take over the dialogue when the system runs into trouble communicating with the user. In this type of handover situation (we call it intervention), it is useful for the operator to have access to the dialogue summary. However, it is not clear exactly what type of summary would be the most useful for a smooth handover. In this study, we investigated the optimal type of summary through experiments in which interlocutors were presented with various summary types during interventions in order to examine their effects. Our findings showed that the best summaries were an abstractive summary plus one utterance immediately before the handover and an extractive summary consisting of five utterances immediately before the handover. From the viewpoint of computational cost, we recommend that extractive summaries consisting of the last five utterances be used.

Despite recent advances, dialogue systems still struggle to achieve fully autonomous transactions. Therefore, when a system encounters a problem, human operators need to take over the dialogue to complete the transaction. However, it is unclear what information should be presented to the operator when this handover takes place. In this study, we conducted a data collection experiment in which one of two operators talked to a user and switched with the other operator periodically while exchanging notes when the handovers took place. By examining these notes, it is possible to identify the information necessary for handing over the dialogue. We collected 60 dialogues in which two operators switched periodically while performing chat, consultation, and sales tasks in dialogue. We found that adjacency pairs are a useful representation for recording conversation history. In addition, we found that key-value-pair representation is also useful when there are underlying tasks, such as consultation and sales.