Dialog state tracking for interview coaching using two-level LSTM

Ming Hsiang Su, Chung-Hsien Wu, Kun Yi Huang, Tsung Hsien Yang, Tsui Ching Huang

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

2 Citations (Scopus)

Abstract

This study presents an approach to dialog state tracking (DST) in an interview conversation by using the long short-term memory (LSTM) and artificial neural network (ANN). First, the techniques of word embedding are employed for word representation by using the word2vec model. Then, each input sentence is represented by a sentence hidden vector using the LSTM-based sentence model. The sentence hidden vectors for each sentence are fed to the LSTM-based answer model to map the interviewee's answer to an answer hidden vector. For dialog state detection, the answer hidden vector is finally used to detect the dialog state using an ANN-based dialog state detection model. To evaluate the proposed method, an interview conversation system was constructed, and an average accuracy of 89.93% was obtained for dialog state detection.

Original languageEnglish
Title of host publicationProceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016
EditorsHsin-Min Wang, Qingzhi Hou, Yuan Wei, Tan Lee, Jianguo Wei, Lei Xie, Hui Feng, Jianwu Dang, Jianwu Dang
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509042937
DOIs
Publication statusPublished - 2017 May 2
Event10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016 - Tianjin, China
Duration: 2016 Oct 172016 Oct 20

Publication series

NameProceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016

Other

Other10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016
CountryChina
CityTianjin
Period16-10-1716-10-20

Fingerprint

coaching
dialogue
interview
neural network
Neural networks
conversation
Long short-term memory

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Linguistics and Language

Cite this

Su, M. H., Wu, C-H., Huang, K. Y., Yang, T. H., & Huang, T. C. (2017). Dialog state tracking for interview coaching using two-level LSTM. In H-M. Wang, Q. Hou, Y. Wei, T. Lee, J. Wei, L. Xie, H. Feng, J. Dang, ... J. Dang (Eds.), Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016 [7918438] (Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCSLP.2016.7918438
Su, Ming Hsiang ; Wu, Chung-Hsien ; Huang, Kun Yi ; Yang, Tsung Hsien ; Huang, Tsui Ching. / Dialog state tracking for interview coaching using two-level LSTM. Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016. editor / Hsin-Min Wang ; Qingzhi Hou ; Yuan Wei ; Tan Lee ; Jianguo Wei ; Lei Xie ; Hui Feng ; Jianwu Dang ; Jianwu Dang. Institute of Electrical and Electronics Engineers Inc., 2017. (Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016).
@inproceedings{175b275b89e042fba7627c77938674e7,
title = "Dialog state tracking for interview coaching using two-level LSTM",
abstract = "This study presents an approach to dialog state tracking (DST) in an interview conversation by using the long short-term memory (LSTM) and artificial neural network (ANN). First, the techniques of word embedding are employed for word representation by using the word2vec model. Then, each input sentence is represented by a sentence hidden vector using the LSTM-based sentence model. The sentence hidden vectors for each sentence are fed to the LSTM-based answer model to map the interviewee's answer to an answer hidden vector. For dialog state detection, the answer hidden vector is finally used to detect the dialog state using an ANN-based dialog state detection model. To evaluate the proposed method, an interview conversation system was constructed, and an average accuracy of 89.93{\%} was obtained for dialog state detection.",
author = "Su, {Ming Hsiang} and Chung-Hsien Wu and Huang, {Kun Yi} and Yang, {Tsung Hsien} and Huang, {Tsui Ching}",
year = "2017",
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editor = "Hsin-Min Wang and Qingzhi Hou and Yuan Wei and Tan Lee and Jianguo Wei and Lei Xie and Hui Feng and Jianwu Dang and Jianwu Dang",
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Su, MH, Wu, C-H, Huang, KY, Yang, TH & Huang, TC 2017, Dialog state tracking for interview coaching using two-level LSTM. in H-M Wang, Q Hou, Y Wei, T Lee, J Wei, L Xie, H Feng, J Dang & J Dang (eds), Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016., 7918438, Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016, Institute of Electrical and Electronics Engineers Inc., 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016, Tianjin, China, 16-10-17. https://doi.org/10.1109/ISCSLP.2016.7918438

Dialog state tracking for interview coaching using two-level LSTM. / Su, Ming Hsiang; Wu, Chung-Hsien; Huang, Kun Yi; Yang, Tsung Hsien; Huang, Tsui Ching.

Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016. ed. / Hsin-Min Wang; Qingzhi Hou; Yuan Wei; Tan Lee; Jianguo Wei; Lei Xie; Hui Feng; Jianwu Dang; Jianwu Dang. Institute of Electrical and Electronics Engineers Inc., 2017. 7918438 (Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016).

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

TY - GEN

T1 - Dialog state tracking for interview coaching using two-level LSTM

AU - Su, Ming Hsiang

AU - Wu, Chung-Hsien

AU - Huang, Kun Yi

AU - Yang, Tsung Hsien

AU - Huang, Tsui Ching

PY - 2017/5/2

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N2 - This study presents an approach to dialog state tracking (DST) in an interview conversation by using the long short-term memory (LSTM) and artificial neural network (ANN). First, the techniques of word embedding are employed for word representation by using the word2vec model. Then, each input sentence is represented by a sentence hidden vector using the LSTM-based sentence model. The sentence hidden vectors for each sentence are fed to the LSTM-based answer model to map the interviewee's answer to an answer hidden vector. For dialog state detection, the answer hidden vector is finally used to detect the dialog state using an ANN-based dialog state detection model. To evaluate the proposed method, an interview conversation system was constructed, and an average accuracy of 89.93% was obtained for dialog state detection.

AB - This study presents an approach to dialog state tracking (DST) in an interview conversation by using the long short-term memory (LSTM) and artificial neural network (ANN). First, the techniques of word embedding are employed for word representation by using the word2vec model. Then, each input sentence is represented by a sentence hidden vector using the LSTM-based sentence model. The sentence hidden vectors for each sentence are fed to the LSTM-based answer model to map the interviewee's answer to an answer hidden vector. For dialog state detection, the answer hidden vector is finally used to detect the dialog state using an ANN-based dialog state detection model. To evaluate the proposed method, an interview conversation system was constructed, and an average accuracy of 89.93% was obtained for dialog state detection.

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M3 - Conference contribution

T3 - Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016

BT - Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016

A2 - Wang, Hsin-Min

A2 - Hou, Qingzhi

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PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Su MH, Wu C-H, Huang KY, Yang TH, Huang TC. Dialog state tracking for interview coaching using two-level LSTM. In Wang H-M, Hou Q, Wei Y, Lee T, Wei J, Xie L, Feng H, Dang J, Dang J, editors, Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016. Institute of Electrical and Electronics Engineers Inc. 2017. 7918438. (Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016). https://doi.org/10.1109/ISCSLP.2016.7918438