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Dialog State Tracking and Action Selection Using Deep Learning Mechanism for Interview Coaching

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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Paper Details

Authors:
Ming-Hsiang Su, Kun-Yi Huang, Tsung-Hsien Yang, Kuan-Jung Lai and Chung-Hsien Wu
Submitted On:
22 November 2016 - 11:33pm
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Type:
Presentation Slides
Event:
Presenter's Name:
Ming-Hsiang Su
Document Year:
2016
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MingHsiangSu-IALP 2016.pdf

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[1] Ming-Hsiang Su, Kun-Yi Huang, Tsung-Hsien Yang, Kuan-Jung Lai and Chung-Hsien Wu, "Dialog State Tracking and Action Selection Using Deep Learning Mechanism for Interview Coaching", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1301. Accessed: Jun. 25, 2017.
@article{1301-16,
url = {http://sigport.org/1301},
author = {Ming-Hsiang Su; Kun-Yi Huang; Tsung-Hsien Yang; Kuan-Jung Lai and Chung-Hsien Wu },
publisher = {IEEE SigPort},
title = {Dialog State Tracking and Action Selection Using Deep Learning Mechanism for Interview Coaching},
year = {2016} }
TY - EJOUR
T1 - Dialog State Tracking and Action Selection Using Deep Learning Mechanism for Interview Coaching
AU - Ming-Hsiang Su; Kun-Yi Huang; Tsung-Hsien Yang; Kuan-Jung Lai and Chung-Hsien Wu
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1301
ER -
Ming-Hsiang Su, Kun-Yi Huang, Tsung-Hsien Yang, Kuan-Jung Lai and Chung-Hsien Wu. (2016). Dialog State Tracking and Action Selection Using Deep Learning Mechanism for Interview Coaching. IEEE SigPort. http://sigport.org/1301
Ming-Hsiang Su, Kun-Yi Huang, Tsung-Hsien Yang, Kuan-Jung Lai and Chung-Hsien Wu, 2016. Dialog State Tracking and Action Selection Using Deep Learning Mechanism for Interview Coaching. Available at: http://sigport.org/1301.
Ming-Hsiang Su, Kun-Yi Huang, Tsung-Hsien Yang, Kuan-Jung Lai and Chung-Hsien Wu. (2016). "Dialog State Tracking and Action Selection Using Deep Learning Mechanism for Interview Coaching." Web.
1. Ming-Hsiang Su, Kun-Yi Huang, Tsung-Hsien Yang, Kuan-Jung Lai and Chung-Hsien Wu. Dialog State Tracking and Action Selection Using Deep Learning Mechanism for Interview Coaching [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1301