Title
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.
Year
DOI
Venue
2016
10.1109/IALP.2016.7875922
2016 International Conference on Asian Language Processing (IALP)
Keywords
Field
DocType
interview coaching system,deep reinforcement learning,LSTM
Dialog box,Data visualization,Structured interview,Computer science,Interview,Coaching,Artificial intelligence,Natural language processing,Deep learning,Action selection,Semantics
Conference
ISSN
ISBN
Citations 
2159-1962
978-1-5090-0923-7
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Ming-Hsiang Su1216.83
Kun-Yi Huang2145.00
Tsung-Hsien Yang311.02
Kuan-Jung Lai400.34
Chung-Hsien Wu51099116.79