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Paper Abstract and Keywords
Presentation 2022-02-21 10:45
User Satisfaction Prediction for Dialogue System in Mental Health Interventions
Shengzhou Yi (UTokyo), Toshiaki Kikuchi (Keio), Toshihiko Yamasaki (UTokyo)
Abstract (in Japanese) (See Japanese page) 
(in English) Mental health conditions deeply impact all areas of the life. Too much stress, most of which is related to work performance and interpersonal relationships, can lead to mental health disorders. Especially, under the influence of COVID-19, people have less chance to communicate with others, and it has become more difficult to get professional help face-to-face for improving mental health. Therefore, remote and automatic dialogue systems have been used for mental health interventions. The system can listen to people’s worries and help them relive stress. In order to provide appropriate support for different types of users’ worries, machine learning techniques were used to discover the topics and profound. In the end of using the dialogue system, the users were asked whether they are satisfied with the experience. According to the user satisfaction, we can maker clear which parts of the dialogue flow should be improved by using natural language models. They were used to simulate and continuously predict the user satisfaction. By observing how the predicted values change after the users answer each predetermined question, the inappropriate parts can be found because they tend to decrease the user satisfaction. Among the language models used in our experiments, BERT showed the highest validation accuracy of 76.04% for the user satisfaction prediction.
Keyword (in Japanese) (See Japanese page) 
(in English) Mental Health / Dialogue System / Language Model / Topic Model / / / /  
Reference Info. ITE Tech. Rep.
Paper #  
Date of Issue  
ISSN Print edition: ISSN 1342-6893  Online edition: ISSN 2424-1970
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Conference Information
Committee AIT ME MMS IEICE-IE IEICE-ITS  
Conference Date 2022-02-21 - 2022-02-22 
Place (in Japanese) (See Japanese page) 
Place (in English) online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IEICE-IE 
Conference Code 2022-02-IE-ITS-AIT-ME-MMS 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) User Satisfaction Prediction for Dialogue System in Mental Health Interventions 
Sub Title (in English)  
Keyword(1) Mental Health  
Keyword(2) Dialogue System  
Keyword(3) Language Model  
Keyword(4) Topic Model  
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1st Author's Name Shengzhou Yi  
1st Author's Affiliation The University of Tokyo (UTokyo)
2nd Author's Name Toshiaki Kikuchi  
2nd Author's Affiliation Keio University (Keio)
3rd Author's Name Toshihiko Yamasaki  
3rd Author's Affiliation The University of Tokyo (UTokyo)
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Speaker
Date Time 2022-02-21 10:45:00 
Presentation Time 15 
Registration for IEICE-IE 
Paper #  
Volume (vol) 46 
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