Dialog Action Decision Using Deep Reinforcement Learning for Question Generation in an Interview Coaching System

Abstract

We often have the interview opportunity when we try to pursue a higher education or find a job 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 the questions An interview coaching system is designed to simulate an interviewer to provide mock interview practice simulation sessions The previous interview coaching systems provided the information including facial preference head nodding and shaking response time and volume etc to let the users know their own performance in the simulated interview However most of these systems need a sufficient number of dialog data and generally only provide the pre-designed interview questions In this thesis we propose an approach to dialog action detection based on deep reinforcement learning for the interview coaching system In dialog action detection deep reinforcement learning is adopted to learn the relation between dialog states and dialog actions based on the labeled dialog states and actions in a collected corpus In interview question generation a tree-based decision is used to choose a proper question template based on the obtained dialog state and the action For training and evaluation twelve participants were invited to provide the interview corpus In total there were 75 dialog consisting of 540 question-answer pairs in the corpus In order to evaluate the ability of deep reinforcement learning we tested 1000 simulated dialogs The average number of completed slots and average number of turns were used for evaluation The assumption for this evaluation is the fewer the dialog turns needed to complete the interview the better the coaching system Based on this criterion the proposed method outperformed other approaches In question generation we validated the relevance model for choosing the best interview question from the decision tree by using 5-fold validation We observe that the proposed system achieved good accuracy and relevance score when 128 nodes in hidden layers of the LSTM was used For the evaluation of the whole interview system we modified the evaluation criteria provided by Traum and a questionnaire is used based on 10 test dialogs Five subjects were invited to score the Naturalness and Utility According to these scores our model achieved an encouraging and acceptable performance

Date of Award	2016 Sept 1
Original language	English
Supervisor	Chung-Hsien Wu (Supervisor)