Dialog State Tracking and action selection using deep learning mechanism for interview coaching

Ming Hsiang Su, Kun Yi Huang, Tsung Hsien Yang, Kuan Jung Lai, Chung Hsien Wu

研究成果: Conference contribution

2 引文 (Scopus)

摘要

The best way to prepare for an interview is to review the different types of possible interview questions you will be asked during an interview and practice responding to questions. An interview coaching system tries to simulate an interviewer to provide mock interview practice simulation sessions for the users. The traditional interview coaching systems provide some feedbacks, including facial preference, head nodding, response time, speaking rate, and volume, to let users know their own performance in the mock interview. But most of these systems are trained with insufficient dialog data and provide the pre-designed interview questions. In this study, we propose an approach to dialog state tracking and action selection based on deep learning methods. First, the interview corpus in this study is collected from 12 participants, and is annotated with dialog states and actions. Next, a long-short term memory and an artificial neural network are employed to predict dialog states and the Deep RL is adopted to learn the relation between dialog states and actions. Finally, the selected action is used to generate the interview question for interview practice. To evaluate the proposed method in action selection, an interview coaching system is constructed. Experimental results show the effectiveness of the proposed method for dialog state tracking and action selection.

原文English
主出版物標題Proceedings of the 2016 International Conference on Asian Language Processing, IALP 2016
編輯Minghui Dong, Chung-Hsien Wu, Yanfeng Lu, Haizhou Li, Yuen-Hsien Tseng, Liang-Chih Yu, Lung-Hao Lee
發行者Institute of Electrical and Electronics Engineers Inc.
頁面6-9
頁數4
ISBN(電子)9781509009213
DOIs
出版狀態Published - 2017 三月 10
事件20th International Conference on Asian Language Processing, IALP 2016 - Tainan, Taiwan
持續時間: 2016 十一月 212016 十一月 23

出版系列

名字Proceedings of the 2016 International Conference on Asian Language Processing, IALP 2016

Other

Other20th International Conference on Asian Language Processing, IALP 2016
國家Taiwan
城市Tainan
期間16-11-2116-11-23

指紋

coaching
dialogue
Neural networks
Feedback
interview
learning
Deep learning
Long short-term memory
learning method
neural network
speaking

All Science Journal Classification (ASJC) codes

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

引用此文

Su, M. H., Huang, K. Y., Yang, T. H., Lai, K. J., & Wu, C. H. (2017). Dialog State Tracking and action selection using deep learning mechanism for interview coaching. 於 M. Dong, C-H. Wu, Y. Lu, H. Li, Y-H. Tseng, L-C. Yu, & L-H. Lee (編輯), Proceedings of the 2016 International Conference on Asian Language Processing, IALP 2016 (頁 6-9). [7875922] (Proceedings of the 2016 International Conference on Asian Language Processing, IALP 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IALP.2016.7875922
Su, Ming Hsiang ; Huang, Kun Yi ; Yang, Tsung Hsien ; Lai, Kuan Jung ; Wu, Chung Hsien. / Dialog State Tracking and action selection using deep learning mechanism for interview coaching. Proceedings of the 2016 International Conference on Asian Language Processing, IALP 2016. 編輯 / Minghui Dong ; Chung-Hsien Wu ; Yanfeng Lu ; Haizhou Li ; Yuen-Hsien Tseng ; Liang-Chih Yu ; Lung-Hao Lee. Institute of Electrical and Electronics Engineers Inc., 2017. 頁 6-9 (Proceedings of the 2016 International Conference on Asian Language Processing, IALP 2016).
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abstract = "The best way to prepare for an interview is to review the different types of possible interview questions you will be asked during an interview and practice responding to questions. An interview coaching system tries to simulate an interviewer to provide mock interview practice simulation sessions for the users. The traditional interview coaching systems provide some feedbacks, including facial preference, head nodding, response time, speaking rate, and volume, to let users know their own performance in the mock interview. But most of these systems are trained with insufficient dialog data and provide the pre-designed interview questions. In this study, we propose an approach to dialog state tracking and action selection based on deep learning methods. First, the interview corpus in this study is collected from 12 participants, and is annotated with dialog states and actions. Next, a long-short term memory and an artificial neural network are employed to predict dialog states and the Deep RL is adopted to learn the relation between dialog states and actions. Finally, the selected action is used to generate the interview question for interview practice. To evaluate the proposed method in action selection, an interview coaching system is constructed. Experimental results show the effectiveness of the proposed method for dialog state tracking and action selection.",
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Su, MH, Huang, KY, Yang, TH, Lai, KJ & Wu, CH 2017, Dialog State Tracking and action selection using deep learning mechanism for interview coaching. 於 M Dong, C-H Wu, Y Lu, H Li, Y-H Tseng, L-C Yu & L-H Lee (編輯), Proceedings of the 2016 International Conference on Asian Language Processing, IALP 2016., 7875922, Proceedings of the 2016 International Conference on Asian Language Processing, IALP 2016, Institute of Electrical and Electronics Engineers Inc., 頁 6-9, 20th International Conference on Asian Language Processing, IALP 2016, Tainan, Taiwan, 16-11-21. https://doi.org/10.1109/IALP.2016.7875922

Dialog State Tracking and action selection using deep learning mechanism for interview coaching. / Su, Ming Hsiang; Huang, Kun Yi; Yang, Tsung Hsien; Lai, Kuan Jung; Wu, Chung Hsien.

Proceedings of the 2016 International Conference on Asian Language Processing, IALP 2016. 編輯 / Minghui Dong; Chung-Hsien Wu; Yanfeng Lu; Haizhou Li; Yuen-Hsien Tseng; Liang-Chih Yu; Lung-Hao Lee. Institute of Electrical and Electronics Engineers Inc., 2017. p. 6-9 7875922 (Proceedings of the 2016 International Conference on Asian Language Processing, IALP 2016).

研究成果: Conference contribution

TY - GEN

T1 - Dialog State Tracking and action selection using deep learning mechanism for interview coaching

AU - Su, Ming Hsiang

AU - Huang, Kun Yi

AU - Yang, Tsung Hsien

AU - Lai, Kuan Jung

AU - Wu, Chung Hsien

PY - 2017/3/10

Y1 - 2017/3/10

N2 - The best way to prepare for an interview is to review the different types of possible interview questions you will be asked during an interview and practice responding to questions. An interview coaching system tries to simulate an interviewer to provide mock interview practice simulation sessions for the users. The traditional interview coaching systems provide some feedbacks, including facial preference, head nodding, response time, speaking rate, and volume, to let users know their own performance in the mock interview. But most of these systems are trained with insufficient dialog data and provide the pre-designed interview questions. In this study, we propose an approach to dialog state tracking and action selection based on deep learning methods. First, the interview corpus in this study is collected from 12 participants, and is annotated with dialog states and actions. Next, a long-short term memory and an artificial neural network are employed to predict dialog states and the Deep RL is adopted to learn the relation between dialog states and actions. Finally, the selected action is used to generate the interview question for interview practice. To evaluate the proposed method in action selection, an interview coaching system is constructed. Experimental results show the effectiveness of the proposed method for dialog state tracking and action selection.

AB - The best way to prepare for an interview is to review the different types of possible interview questions you will be asked during an interview and practice responding to questions. An interview coaching system tries to simulate an interviewer to provide mock interview practice simulation sessions for the users. The traditional interview coaching systems provide some feedbacks, including facial preference, head nodding, response time, speaking rate, and volume, to let users know their own performance in the mock interview. But most of these systems are trained with insufficient dialog data and provide the pre-designed interview questions. In this study, we propose an approach to dialog state tracking and action selection based on deep learning methods. First, the interview corpus in this study is collected from 12 participants, and is annotated with dialog states and actions. Next, a long-short term memory and an artificial neural network are employed to predict dialog states and the Deep RL is adopted to learn the relation between dialog states and actions. Finally, the selected action is used to generate the interview question for interview practice. To evaluate the proposed method in action selection, an interview coaching system is constructed. Experimental results show the effectiveness of the proposed method for dialog state tracking and action selection.

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M3 - Conference contribution

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T3 - Proceedings of the 2016 International Conference on Asian Language Processing, IALP 2016

SP - 6

EP - 9

BT - Proceedings of the 2016 International Conference on Asian Language Processing, IALP 2016

A2 - Dong, Minghui

A2 - Wu, Chung-Hsien

A2 - Lu, Yanfeng

A2 - Li, Haizhou

A2 - Tseng, Yuen-Hsien

A2 - Yu, Liang-Chih

A2 - Lee, Lung-Hao

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Su MH, Huang KY, Yang TH, Lai KJ, Wu CH. Dialog State Tracking and action selection using deep learning mechanism for interview coaching. 於 Dong M, Wu C-H, Lu Y, Li H, Tseng Y-H, Yu L-C, Lee L-H, 編輯, Proceedings of the 2016 International Conference on Asian Language Processing, IALP 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 6-9. 7875922. (Proceedings of the 2016 International Conference on Asian Language Processing, IALP 2016). https://doi.org/10.1109/IALP.2016.7875922