Dialogue systems need to accurately understand the user’s mental state to generate appropriate responses, but accurately discerning such states solely from text or speech can be challenging. To determine which information is necessary, we first collected human-human multimodal dialogues using heterogeneous sensors, resulting in a dataset containing various types of information including speech, video, physiological signals, gaze, and body movement. Additionally, for each time step of the data, users provided subjective evaluations of their emotional valence while reviewing the dialogue videos. Using this dataset and focusing on physiological signals, we analyzed the relationship between the signals and the subjective evaluations through Granger causality analysis. We also investigated how sensor signals differ depending on the polarity of the valence. Our findings revealed several physiological signals related to the user’s emotional valence.

While task-oriented dialogue systems have improved, not all users can fully accomplish their tasks. Users with limited knowledge about the system may experience dialogue breakdowns or fail to achieve their tasks because they do not know how to interact with the system. For addressing this issue, it would be desirable to construct a system that can estimate the user’s task success ability and adapt to that ability. In this study, we propose a method that estimates this ability by applying item response theory (IRT), commonly used in education for estimating examinee abilities, to task-oriented dialogue systems. Through experiments predicting the probability of a correct answer to each slot by using the estimated task success ability, we found that the proposed method significantly outperformed baselines.

Endowing a task-oriented dialogue system with adaptiveness to user personality can greatly help improve the performance of a dialogue task. However, such a dialogue system can be practically challenging to implement, because it is unclear how user personality influences dialogue task performance. To explore the relationship between user personality and dialogue task performance, we enrolled participants via crowdsourcing to first answer specified personality questionnaires and then chat with a dialogue system to accomplish assigned tasks. A rule-based dialogue system on the prevalent Multi-Domain Wizard-of-Oz (MultiWOZ) task was used. A total of 211 participants’ personalities and their 633 dialogues were collected and analyzed. The results revealed that sociable and extroverted people tended to fail the task, whereas neurotic people were more likely to succeed. We extracted features related to user dialogue behaviors and performed further analysis to determine which kind of behavior influences task performance. As a result, we identified that average utterance length and slots per utterance are the key features of dialogue behavior that are highly correlated with both task performance and user personality.