Ao Guo


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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

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Applying Item Response Theory to Task-oriented Dialogue Systems for Accurately Determining User’s Task Success Ability
Ryu Hirai | Ao Guo | Ryuichiro Higashinaka
Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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.


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Influence of user personality on dialogue task performance: A case study using a rule-based dialogue system
Ao Guo | Atsumoto Ohashi | Ryu Hirai | Yuya Chiba | Yuiko Tsunomori | Ryuichiro Higashinaka
Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI

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.