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Title: Joint dialog act segmentation and recognition in human conversations using attention to dialog context
Authors: Zhao, Tianyu
Kawahara, Tatsuya  kyouindb  KAKEN_id  orcid (unconfirmed)
Author's alias: 河原, 達也
Keywords: Spoken dialog system
Spoken language understanding
Dialog act segmentation
Dialog act recognition
Issue Date: Sep-2019
Publisher: Elsevier BV
Journal title: Computer Speech and Language
Volume: 57
Start page: 108
End page: 127
Abstract: A dialog act represents the communicative function of an utterance in a conversation, and thus provides informative cues for understanding, managing, and generating dialog. While most spoken dialog systems process user input and system output at the turn level, a single turn can consist of multiple dialog acts in human conversations. Therefore, segmenting turn-level tokens into a meaningful dialog act unit is just as important as recognizing the dialog act. Towards joint segmentation and recognition of dialog acts, we propose an encoder–decoder model featuring joint coding and incorporate contextual information by means of an attentional mechanism. The proposed encoder–decoder outperforms other models in segmentation, and the application of attentions significantly reduces recognition error rates. By combining the encoder–decoder model with contextual attention, we achieve state-of-the-art performance in the joint evaluation of dialog act segmentation and recognition.
Rights: © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
The full-text file will be made open to the public on 1 September 2021 in accordance with publisher's 'Terms and Conditions for Self-Archiving'.
This is not the published version. Please cite only the published version.
DOI(Published Version): 10.1016/j.csl.2019.03.001
Appears in Collections:Journal Articles

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