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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/cyberscitech68397.2025.00059

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

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).

DOI： 10.1038/s41598-025-07101-7

Open Access

PubMed

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1145/3765766.3765821

Open Access

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/swc62898.2024.00033

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/cyberscitech64112.2024.00048

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Other Link： https://dblp.uni-trier.de/db/conf/cyberscitech/cyberscitech2024.html#MaJHVGFG24

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/picom64201.2024.00009

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Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/cyberscitech64112.2024.00087

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Other Link： https://dblp.uni-trier.de/db/conf/cyberscitech/cyberscitech2024.html#HuangMGBM24

Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1038/s41598-024-53989-y

Open Access

Scopus

PubMed

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Association for the Advancement of Artificial Intelligence (AAAI)  

Although large language models such as ChatGPT and GPT-4 have achieved superb performances in various natural language processing tasks, their dialogue performance is sometimes not very clear because the evaluation is often done on the utterance level where the quality of an utterance given context is the evaluation target. Our objective in this work is to conduct human evaluations of GPT-3.5 and GPT-4 to perform MultiWOZ and persona-based chat tasks in order to verify their dialogue-level performance in task-oriented and non-task-oriented dialogue systems. Our findings show that GPT-4 performs comparably with a carefully created rule-based system and has a significantly superior performance to other systems, including those based on GPT-3.5, in persona-based chat.

DOI： 10.1609/aaaiss.v2i1.27668

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Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/SMC54092.2024.10831453

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Other Link： https://dblp.uni-trier.de/db/conf/smc/smc2024.html#GuoHMM24

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Association for Computational Linguistics  

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Other Link： https://dblp.uni-trier.de/rec/conf/sigdial/2024

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Association for Computational Linguistics  

DOI： 10.18653/v1/2023.sigdial-1.39

Open Access

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Other Link： https://dblp.uni-trier.de/rec/conf/sigdial/2023

Language：English  

At the Dialogue Robot Competition 2023 (DRC2023), which was held to improve
the capability of dialogue robots, our team developed a system that could build
common ground and take more natural turns based on user utterance texts. Our
system generated queries for sightseeing spot searches using the common ground
and engaged in dialogue while waiting for user comprehension.

DOI： 10.48550/arXiv.2312.13816

arXiv

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Association for Computational Linguistics  

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Other Link： https://dblp.uni-trier.de/rec/conf/paclic/2023

Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/RO-MAN57019.2023.10309654

Scopus

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Other Link： https://dblp.uni-trier.de/db/conf/ro-man/ro-man2023.html#GuoOCTHH23

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics60724.2023.00090

Scopus

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Other Link： https://dblp.uni-trier.de/db/conf/ithings/ithings2023.html#TakiMGMQ23

Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/DASC/PiCom/CBDCom/Cy59711.2023.10361426

Scopus

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Other Link： https://dblp.uni-trier.de/db/conf/dasc/dasc2023.html#GuoBM23

Language：English  

To improve the interactive capabilities of a dialogue system, e.g., to adapt
to different customers, the Dialogue Robot Competition (DRC2022) was held. As
one of the teams, we built a dialogue system with a pipeline structure
containing four modules. The natural language understanding (NLU) and natural
language generation (NLG) modules were GPT-2 based models, and the dialogue
state tracking (DST) and policy modules were designed on the basis of
hand-crafted rules. After the preliminary round of the competition, we found
that the low variation in training examples for the NLU and failed
recommendation due to the policy used were probably the main reasons for the
limited performance of the system.

DOI： 10.48550/arXiv.2210.09518

arXiv

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.ijpe.2020.107963

Scopus

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)  

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00092

Scopus

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Other Link： https://dblp.uni-trier.de/db/conf/ithings/ithings2020.html#JiangGM20

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00058

Scopus

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Other Link： https://dblp.uni-trier.de/db/conf/dasc/dasc2020.html#JiangGM20

Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00059

Scopus

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Other Link： https://dblp.uni-trier.de/db/conf/dasc/dasc2020.html#GuoJM20

Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1007/s11280-019-00713-w

Scopus

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.compeleceng.2019.106544

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00213

Scopus

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Other Link： https://dblp.uni-trier.de/db/conf/uic/uic2019.html#SunGMW19

Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00043

Scopus

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Other Link： https://dblp.uni-trier.de/db/conf/dasc/dasc2019.html#GuoMW19

Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00033

Scopus

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Other Link： https://dblp.uni-trier.de/db/conf/ithings/ithings2019.html#GuoMW19

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00034

Scopus

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Other Link： https://dblp.uni-trier.de/db/conf/ithings/ithings2019.html#TanGMR19

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE Computer Society  

DOI： 10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00023

Scopus

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Other Link： https://dblp.uni-trier.de/db/conf/dasc/dasc2018.html#CaiGMHYY18

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE Computer Society  

DOI： 10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00020

Scopus

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Other Link： https://dblp.uni-trier.de/db/conf/dasc/dasc2018.html#TsanGMHC18

Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE Computer Society  

DOI： 10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00021

Scopus

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Other Link： https://dblp.uni-trier.de/db/conf/dasc/dasc2018.html#GuoMW18

Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/UIC-ATC.2017.8397520

Scopus

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Other Link： https://dblp.uni-trier.de/db/conf/uic/uic2017.html#GuoM17

Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.3390/s18030684

Open Access

Scopus

PubMed

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Other Link： https://dblp.uni-trier.de/db/journals/sensors/sensors18.html#GuoM18

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/CYBConf.2017.7985758

Scopus

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Other Link： https://dblp.uni-trier.de/db/conf/cybconf/cybconf2017.html#XuGM17

Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE Computer Society  

DOI： 10.1109/DASC-PICom-DataCom-CyberSciTec.2017.19

Scopus

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Other Link： https://dblp.uni-trier.de/db/conf/dasc/dasc2017.html#GuoM17

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE Computer Society  

DOI： 10.1109/DASC-PICom-DataCom-CyberSciTec.2017.34

Scopus

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Other Link： https://dblp.uni-trier.de/db/conf/dasc/dasc2017.html#XuGMW17

Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/iThings-GreenCom-CPSCom-SmartData.2016.121

Scopus

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Other Link： https://dblp.uni-trier.de/db/conf/ithings/ithings2016.html#GuoM16

Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/ACCESS.2017.2666419

Open Access

Scopus

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/CIT/IUCC/DASC/PICOM.2015.314

Scopus

CiNii Research

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Other Link： https://dblp.uni-trier.de/db/conf/IEEEcit/cit-iucc-dasc-picom2015.html#GuoM15

Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)   Publisher：The Japanese Society for Artificial Intelligence  

Detecting drivers' drowsiness with high accuracy and fine granularity is essential to ensure road safety. With the growing popularity of wearable devices, various physiological signals have become accessible, enabling drowsiness detection anywhere and at any time. Recent studies have achieved multilevel drowsiness detection, identifying up to eight drowsiness states using a single ECG signal. However, the effectiveness of using multiple physiological signals remains unclear. To address this, this study conducted four types of drowsiness detection, each with varying granularity, by utilizing ECG and EMG signals from the DROZY dataset. We first built models for each single modality using CNN and LSTM, optimizing model parameters to identify the best-performing models for each modality. We then built a multimodal model by concatenating the best-performing models for the two modalities. As a result, for fine-granularity drowsiness detection, using multimodal signals outperformed detection only using a single modality of signals. In addition, the optimized model for multilevel drowsiness classification is also identified.

DOI： 10.11517/pjsai.jsai2024.0_1b5gs203

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Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)  

J-GLOBAL

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Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)  

J-GLOBAL

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Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)   Publisher：The Japanese Society for Artificial Intelligence  

Current research on emotion recognition has mainly focused on content dependent emotion recognition, where a model is trained and tested using user data from the same content sources (e.g., watch a movie or play a game). To provide cross-content services due to users’ emotions anywhere, it is necessary for a model to recognize users’ emotions in different content sources (i.e., content independent). Since limited studies have focused on content independent recognition, whether such emotion recognition has a competitive performance with content dependent emotion recognition is still unclear. To address this issue, we performed a comparative study of content dependent and independent emotion recognition by building CNN-based models from DEAP dataset. The DEAP dataset contains physiological data collected from 32 individuals while they were watching different videos. The data collected while watching a specific video is regarded as a single content. We built content independent model with leave-one-content-out approach. That is, using physiological data from one specific content for testing, and using the data from the remaining contents for training. As a result, we noticed that the performance of content independent recognition is significantly lower than that of content dependent recognition. We also identified that users’ emotions can be easily recognized in certain contents.

DOI： 10.11517/pjsai.jsai2024.0_4q3is2d02

J-GLOBAL

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Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)   Publisher：The Japanese Society for Artificial Intelligence  

Although the performance of task-oriented dialogue systems has been improving, not all users are able to accomplish their tasks perfectly. In particular, users with little knowledge of the system may not know how to effectively communicate with the system, resulting in dialogue breakdowns and failure to accomplish the task. In this study, we aim to improve the system's understanding of user utterances by providing a tutorial at the beginning of a dialogue to notify users of the type of utterances that the system can understand. We built a tutorial system using the MultiWOZ dataset and verified its effectiveness through utterance understanding experiments with human users.

DOI： 10.11517/pjsai.jsai2022.0_2f4gs903

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