ID | 116757 |
Author |
Matsumoto, Kazuyuki
Tokushima University
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Sasayama, Manabu
National Institute of Technology, Kagawa College
Yoshida, Minoru
Tokushima University
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Kita, Kenji
Tokushima University
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Ren, Fuji
Tokushima University
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Keywords | natural language processing
dialogue breakdown
human-computer dialogue system
sentiment analysis
emotion recognition
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Content Type |
Journal Article
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Description | In dialogues between robots or computers and humans, dialogue breakdown analysis is an important tool for achieving better chat dialogues. Conventional dialogue breakdown detection methods focus on semantic variance. Although these methods can detect dialogue breakdowns based on semantic gaps, they cannot always detect emotional breakdowns in dialogues. In chat dialogue systems, emotions are sometimes included in the utterances of the system when responding to the speaker. In this study, we detect emotions from utterances, analyze emotional changes, and use them as the dialogue breakdown feature. The proposed method estimates emotions by utterance unit and generates features by calculating the similarity of the emotions of the utterance and the emotions that have appeared in prior utterances. We employ deep neural networks using sentence distributed representation vectors as the feature. In an evaluation of experimental results, the proposed method achieved a higher dialogue breakdown detection rate when compared to the method using a sentence distributed representation vectors.
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Journal Title |
Electronics
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ISSN | 20799292
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Publisher | MDPI
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Volume | 11
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Issue | 5
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Start Page | 695
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Published Date | 2022-02-24
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Rights | This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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DOI (Published Version) | |
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language |
eng
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Publisher
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departments |
Science and Technology
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