Attention-Based Dialog State Tracking for Conversational Interview Coaching

Ming Hsiang Su, Chung-Hsien Wu, Kun Yi Huang, Chu Kwang Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This study proposes an approach to dialog state tracking (DST) in a conversational interview coaching system. For the interview coaching task, the semantic slots, used mostly in traditional dialog systems, are difficult to define manually. This study adopts the topic profile of the response from the interviewee as the dialog state representation. In addition, as the response generally consists of several sentences, the summary vector obtained from a long short-term memory neural network (LSTM) is likely to contain noisy information from many irrelevant sentences. This study proposes a sentence attention mechanism combining the sentence attention weights from a convolutional neural tensor network (CNTN) and the topic profile by selectively focusing on significant sentences for attention-based dialog state tracking. This study collected 260 interview dialogs consisting of 3,016 dialog turns for performance evaluation. A five-fold cross validation scheme was employed and the results show that the proposed method outperformed the semantic slot-based baseline method.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6144-6148
Number of pages5
Volume2018-April
ISBN (Print)9781538646588
DOIs
Publication statusPublished - 2018 Sep 10
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: 2018 Apr 152018 Apr 20

Other

Other2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
CountryCanada
CityCalgary
Period18-04-1518-04-20

Fingerprint

Semantics
Tensors
Neural networks
Long short-term memory

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Su, M. H., Wu, C-H., Huang, K. Y., & Chen, C. K. (2018). Attention-Based Dialog State Tracking for Conversational Interview Coaching. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (Vol. 2018-April, pp. 6144-6148). [8461494] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2018.8461494
Su, Ming Hsiang ; Wu, Chung-Hsien ; Huang, Kun Yi ; Chen, Chu Kwang. / Attention-Based Dialog State Tracking for Conversational Interview Coaching. 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Vol. 2018-April Institute of Electrical and Electronics Engineers Inc., 2018. pp. 6144-6148
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Su, MH, Wu, C-H, Huang, KY & Chen, CK 2018, Attention-Based Dialog State Tracking for Conversational Interview Coaching. in 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. vol. 2018-April, 8461494, Institute of Electrical and Electronics Engineers Inc., pp. 6144-6148, 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018, Calgary, Canada, 18-04-15. https://doi.org/10.1109/ICASSP.2018.8461494

Attention-Based Dialog State Tracking for Conversational Interview Coaching. / Su, Ming Hsiang; Wu, Chung-Hsien; Huang, Kun Yi; Chen, Chu Kwang.

2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Vol. 2018-April Institute of Electrical and Electronics Engineers Inc., 2018. p. 6144-6148 8461494.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Su MH, Wu C-H, Huang KY, Chen CK. Attention-Based Dialog State Tracking for Conversational Interview Coaching. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Vol. 2018-April. Institute of Electrical and Electronics Engineers Inc. 2018. p. 6144-6148. 8461494 https://doi.org/10.1109/ICASSP.2018.8461494