Follow-up Question Generation using Pattern-based Seq2seq with a Small Corpus for Interview Coaching

Abstract

Participation in an admission interview is a common critical procedure before entering a school However students rarely seek professional help even if the best approach is to ask questions of an expert interviewer To some degree students lack interview experience and are unable to perform well in such important interviews The reason for this may include both time and financial considerations In light of this in this thesis an interview system is developed that repeatedly trains a student with questions that frequently appear in interview situations In the dialogue system follow-up questions are able to transform policy into words as a representative system response Traditional follow-up question generation focuses on generating sentences which takes a significant amount of time in regard to defining the template and patterns as well as setting the rules Compared to the traditional method word clustering is adopted here to automatically transform the sentences into patterns which simultaneously decreases the complexity of the sentences When answers are composed of numerous patterns CNTN (convolution neural tensor network) is adopted to select the appropriate answer pattern for follow-up pattern generation As in the thesis we apply the CNTN for tracing the proper pattern In order to generate the pattern for a follow-up question we also apply Seq2seq model to learn the relations between the patterns To acquire the candidate follow-up questions we then make the follow-up question patterns fill up with words using the Word Class Table For the last steps we use a Language Model to rank the candidate follow-up questions and choose the most suitable follow-up question as the response for an interviewee In this study 3 390 answer and follow-up question pairs were collected Five-fold cross validation was employed for evaluation The results show that the proposed method performed better than the traditional method Also the results had positive value in terms of both objective and subjective assessment

Date of Award	2018 Jan 23
Original language	English
Supervisor	Chung-Hsien Wu (Supervisor)