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アイテム
Topic Break Detection in Interview Dialogues Using Sentence Embedding of Utterance and Speech Intention Based on Multitask Neural Networks
https://tokushima-u.repo.nii.ac.jp/records/2009648
https://tokushima-u.repo.nii.ac.jp/records/2009648b0685398-479c-4f43-8d11-a572af5ace48
| 名前 / ファイル | ライセンス | アクション |
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sensors_22_2_694.pdf (1.33 MB)
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| Item type | 文献 / Documents(1) | |||||||||||||||||||||||
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| 公開日 | 2022-03-02 | |||||||||||||||||||||||
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| アクセス権 | open access | |||||||||||||||||||||||
| 資源タイプ | ||||||||||||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||||||||||||
| 資源タイプ | journal article | |||||||||||||||||||||||
| 出版社版DOI | ||||||||||||||||||||||||
| 関連識別子 | https://doi.org/10.3390/s22020694 | |||||||||||||||||||||||
| 関連名称 | 10.3390/s22020694 | |||||||||||||||||||||||
| 出版タイプ | ||||||||||||||||||||||||
| 出版タイプ | VoR | |||||||||||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||||||||||||
| タイトル | ||||||||||||||||||||||||
| タイトル | Topic Break Detection in Interview Dialogues Using Sentence Embedding of Utterance and Speech Intention Based on Multitask Neural Networks | |||||||||||||||||||||||
| 著者 |
松本, 和幸
× 松本, 和幸
WEKO
311
× ササヤマ, マナブ
× キリハラ, タイガ
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| 抄録 | ||||||||||||||||||||||||
| 内容記述 | Currently, task-oriented dialogue systems that perform specific tasks based on dialogue are widely used. Moreover, research and development of non-task-oriented dialogue systems are also actively conducted. One of the problems with these systems is that it is difficult to switch topics naturally. In this study, we focus on interview dialogue systems. In an interview dialogue, the dialogue system can take the initiative as an interviewer. The main task of an interview dialogue system is to obtain information about the interviewee via dialogue and to assist this individual in understanding his or her personality and strengths. In order to accomplish this task, the system needs to be flexible and appropriate for detecting topic switching and topic breaks. Given that topic switching tends to be more ambiguous in interview dialogues than in task-oriented dialogues, existing topic modeling methods that determine topic breaks based only on relationships and similarities between words are likely to fail. In this study, we propose a method for detecting topic breaks in dialogue to achieve flexible topic switching in interview dialogue systems. The proposed method is based on multi-task learning neural network that uses embedded representations of sentences to understand the context of the text and utilizes the intention of an utterance as a feature. In multi-task learning, not only topic breaks but also the intention associated with the utterance and the speaker are targets of prediction. The results of our evaluation experiments show that using utterance intentions as features improves the accuracy of topic separation estimation compared to the baseline model. | |||||||||||||||||||||||
| キーワード | ||||||||||||||||||||||||
| 主題 | interview dialogue | |||||||||||||||||||||||
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| 主題 | topic segmentation | |||||||||||||||||||||||
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| 主題 | dialogue analysis | |||||||||||||||||||||||
| 書誌情報 |
en : Sensors 巻 22, 号 2, p. 694, 発行日 2022-01-17 |
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| 収録物識別子タイプ | ISSN | |||||||||||||||||||||||
| 収録物識別子 | 14248220 | |||||||||||||||||||||||
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| 出版者 | MDPI | |||||||||||||||||||||||
| 権利情報 | ||||||||||||||||||||||||
| 権利情報 | 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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| 識別子 | 383885 | |||||||||||||||||||||||
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| 言語 | eng | |||||||||||||||||||||||
