Sanae Yamashita


2025

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Exploring Factors Influencing Hospitality in Mobile Robot Guidance: A Wizard-of-Oz Study with a Teleoperated Humanoid Robot
Ao Guo | Shota Mochizuki | Sanae Yamashita | Saya Nikaido | Tomoko Isomura | Ryuichiro Higashinaka
Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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.

2023

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RealPersonaChat: A Realistic Persona Chat Corpus with Interlocutors’ Own Personalities
Sanae Yamashita | Koji Inoue | Ao Guo | Shota Mochizuki | Tatsuya Kawahara | Ryuichiro Higashinaka
Proceedings of the 37th Pacific Asia Conference on Language, Information and Computation

2022

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Optimal Summaries for Enabling a Smooth Handover in Chat-Oriented Dialogue
Sanae Yamashita | Ryuichiro Higashinaka
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: Student Research Workshop

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.

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Data Collection for Empirically Determining the Necessary Information for Smooth Handover in Dialogue
Sanae Yamashita | Ryuichiro Higashinaka
Proceedings of the Thirteenth Language Resources and Evaluation Conference

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.