角森唯子 – 名古屋大学情報学研究科東中研究室

研究内容

ユーザが対話中に話した内容を踏まえて，ユーザに合わせた対話を行う対話システムの研究を行っています．タスク班では，対話が破綻したときに，対話システムがリカバリを行う方法について研究しています．

経歴

2015年奈良先端科学技術大学院大学情報科学研究科博士前期課程修了
2015年株式会社NTTドコモ入社
2020年より名古屋大学情報学研究科博士後期課程に在籍
2022年よりNTTコミュニケーション科学基礎研究所研究主任
2023年よりJST ACT-X 次世代AIを築く数理・情報科学の革新第1期生

発表

2025

Ao Guo, Atsumoto Ohashi, Ryu Hirai, Yuya Chiba, Yuiko Tsunomori, Ryuichiro Higashinaka

User Personality and its Influence on the Performance of Pipeline and End-to-end Task-oriented Dialogue Systems Journal Article

In: Scientific Reports , vol. 15, no. 1, pp. 20745-20763, 2025, ISBN: 2045-2322.

Abstract | Links | BibTeX

@article{guo-sr-2025,

title = {User Personality and its Influence on the Performance of Pipeline and End-to-end Task-oriented Dialogue Systems},

author = {Ao Guo, Atsumoto Ohashi, Ryu Hirai, Yuya Chiba, Yuiko Tsunomori, Ryuichiro Higashinaka},

url = {https://doi.org/10.1038/s41598-025-07101-7},

doi = {10.1038/s41598-025-07101-7},

isbn = {2045-2322},

year  = {2025},

date = {2025-07-01},

urldate = {2025-07-01},

journal = {Scientific Reports },

volume = {15},

number = {1},

pages = {20745-20763},

abstract = {A task-oriented dialogue system with adaptability to user personality could potentially improve dialogue task performance in terms of task success and user satisfaction. In practice, however, the implementation of such a dialogue system would be challenging because of our limited understanding of how user personality influences dialogue task performance. Understanding these influences is essential for developing dialogue strategies that adapt to user personalities, allowing a dialogue system to cater to different users and thereby improve task performance. To examine these influences, we collected data by enrolling crowd-sourced participants to answer personality questionnaires and then chat with a dialogue system to accomplish assigned dialogue tasks. The dialogue tasks were designed using the MultiWOZ dataset, which covers dialogues between a clerk bot and a customer in the context of tourist information access. To clarify the general influence of personality on dialogue task performance across different dialogue systems, we conducted a comparative analysis using both a pipeline system and an end-to-end (E2E) system. We first explored the correlation among user personality, user dialogue behavior, and dialogue task performance through correlation analysis. The results indicate a weak correlation between user personality and dialogue task performance, along with stronger correlations between dialogue behavior with both user personality and dialogue task performance. On the basis of the results of the correlation analysis, we then used Structural Equation Modeling (SEM) to analyze the relationships between user personality and dialogue task performance, using user dialogue behavior as an intermediate variable. The constructed SEM models showed good fit indices. Our results indicate that neutral perceptual sensitivity (which measures a user’s sensitivity to external stimuli) and emotional management skill (which measures a user’s ability to regulate his/her emotional reactions to situations) have significant influences on his/her dialogue task performance across different dialogue systems. In addition, our findings reveal that personality traits, such as extraversion and conscientiousness, influence dialogue task performance, although such influences may be subject to context-specific variability such as dialogue task design, dialogue domain (MultiWOZ), and system architecture (pipeline).



},

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pubstate = {published},

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A task-oriented dialogue system with adaptability to user personality could potentially improve dialogue task performance in terms of task success and user satisfaction. In practice, however, the implementation of such a dialogue system would be challenging because of our limited understanding of how user personality influences dialogue task performance. Understanding these influences is essential for developing dialogue strategies that adapt to user personalities, allowing a dialogue system to cater to different users and thereby improve task performance. To examine these influences, we collected data by enrolling crowd-sourced participants to answer personality questionnaires and then chat with a dialogue system to accomplish assigned dialogue tasks. The dialogue tasks were designed using the MultiWOZ dataset, which covers dialogues between a clerk bot and a customer in the context of tourist information access. To clarify the general influence of personality on dialogue task performance across different dialogue systems, we conducted a comparative analysis using both a pipeline system and an end-to-end (E2E) system. We first explored the correlation among user personality, user dialogue behavior, and dialogue task performance through correlation analysis. The results indicate a weak correlation between user personality and dialogue task performance, along with stronger correlations between dialogue behavior with both user personality and dialogue task performance. On the basis of the results of the correlation analysis, we then used Structural Equation Modeling (SEM) to analyze the relationships between user personality and dialogue task performance, using user dialogue behavior as an intermediate variable. The constructed SEM models showed good fit indices. Our results indicate that neutral perceptual sensitivity (which measures a user’s sensitivity to external stimuli) and emotional management skill (which measures a user’s ability to regulate his/her emotional reactions to situations) have significant influences on his/her dialogue task performance across different dialogue systems. In addition, our findings reveal that personality traits, such as extraversion and conscientiousness, influence dialogue task performance, although such influences may be subject to context-specific variability such as dialogue task design, dialogue domain (MultiWOZ), and system architecture (pipeline).

2024

角森唯子, 東中竜一郎

パーソナライズ可能な雑談対話システム構築に向けた任意のユーザ情報を踏まえた発話コーパスの構築と分析 Journal Article

In: 自然言語処理, vol. 31, iss. 4, pp. 1635–1664, 2024.

Links | BibTeX

Yuiko Tsunomori, Ryuichiro Higashinaka

I Remember You!: SUI Corpus for Remembering and Utilizing Users' Information in Chat-oriented Dialogue Systems Proceedings Article

In: The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING), 2024.

Links | BibTeX

Ao Guo, Ryu Hirai, Atsumoto Ohashi, Yuya Chiba, Yuiko Tsunomori, Ryuichiro Higashinaka

Personality Prediction from Task-oriented and Open-domain Human–machine Dialogues Journal Article

In: Scientific Reports, vol. 14, iss. 3868, 2024.

Abstract | Links | BibTeX

@article{guo_sr2024,

title = {Personality Prediction from Task-oriented and Open-domain Human–machine Dialogues},

author = {Ao Guo and Ryu Hirai and Atsumoto Ohashi and Yuya Chiba and Yuiko Tsunomori and Ryuichiro Higashinaka},

doi = {https://doi.org/10.1038/s41598-024-53989-y},

year  = {2024},

date = {2024-02-16},

urldate = {2024-02-16},

journal = {Scientific Reports},

volume = {14},

issue = {3868},

abstract = {If a dialogue system can predict the personality of a user from dialogue, it will enable the system to adapt to the user's personality, leading to better task success and user satisfaction. In a recent study, personality prediction was performed using the Myers–Briggs Type Indicator (MBTI) personality traits with a task-oriented human-machine dialogue using an end-to-end (neural-based) system. However, it is still not clear whether such prediction is generally possible for other types of systems and user personality traits. To clarify this, we recruited 378 participants, asked them to fill out four personality questionnaires covering 25 personality traits, and had them perform three rounds of human-machine dialogue with a pipeline task-oriented dialogue system or an end-to-end task-oriented dialogue system. We also had another 186 participants do the same with an open-domain dialogue system. We then constructed BERT-based models to predict the personality traits of the participants from the dialogues. The results showed that prediction accuracy was generally better with open-domain dialogue than with task-oriented dialogue, although Extraversion (one of the Big Five personality traits) could be predicted equally well for both open-domain dialogue and pipeline task-oriented dialogue. We also examined the effect of utilizing different types of dialogue on personality prediction by conducting a cross-comparison of the models trained from the task-oriented and open-domain dialogues. As a result, we clarified that the open-domain dialogue cannot be used to predict personality traits from task-oriented dialogue, and vice versa. We further analyzed the effects of system utterances, task performance, and the round of dialogue with regard to the prediction accuracy.},

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pubstate = {published},

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