| 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. |
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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) |
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| 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) |
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Mental Health |
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Dialogue System |
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Language Model |
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Topic Model |
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| 1st Author's Name |
Shengzhou Yi |
| 1st Author's Affiliation |
The University of Tokyo (UTokyo) |
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Toshiaki Kikuchi |
| 2nd Author's Affiliation |
Keio University (Keio) |
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Toshihiko Yamasaki |
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The University of Tokyo (UTokyo) |
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Author-1 |
| Date Time |
2022-02-21 10:45:00 |
| Presentation Time |
15 minutes |
| Registration for |
IEICE-IE |
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| Volume (vol) |
vol.46 |
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