TY - GEN
T1 - Attention-Based Dialog State Tracking for Conversational Interview Coaching
AU - Su, Ming Hsiang
AU - Wu, Chung Hsien
AU - Huang, Kun Yi
AU - Chen, Chu Kwang
PY - 2018/9/10
Y1 - 2018/9/10
N2 - This study proposes an approach to dialog state tracking (DST) in a conversational interview coaching system. For the interview coaching task, the semantic slots, used mostly in traditional dialog systems, are difficult to define manually. This study adopts the topic profile of the response from the interviewee as the dialog state representation. In addition, as the response generally consists of several sentences, the summary vector obtained from a long short-term memory neural network (LSTM) is likely to contain noisy information from many irrelevant sentences. This study proposes a sentence attention mechanism combining the sentence attention weights from a convolutional neural tensor network (CNTN) and the topic profile by selectively focusing on significant sentences for attention-based dialog state tracking. This study collected 260 interview dialogs consisting of 3,016 dialog turns for performance evaluation. A five-fold cross validation scheme was employed and the results show that the proposed method outperformed the semantic slot-based baseline method.
AB - This study proposes an approach to dialog state tracking (DST) in a conversational interview coaching system. For the interview coaching task, the semantic slots, used mostly in traditional dialog systems, are difficult to define manually. This study adopts the topic profile of the response from the interviewee as the dialog state representation. In addition, as the response generally consists of several sentences, the summary vector obtained from a long short-term memory neural network (LSTM) is likely to contain noisy information from many irrelevant sentences. This study proposes a sentence attention mechanism combining the sentence attention weights from a convolutional neural tensor network (CNTN) and the topic profile by selectively focusing on significant sentences for attention-based dialog state tracking. This study collected 260 interview dialogs consisting of 3,016 dialog turns for performance evaluation. A five-fold cross validation scheme was employed and the results show that the proposed method outperformed the semantic slot-based baseline method.
UR - http://www.scopus.com/inward/record.url?scp=85054233036&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054233036&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2018.8461494
DO - 10.1109/ICASSP.2018.8461494
M3 - Conference contribution
AN - SCOPUS:85054233036
SN - 9781538646588
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 6144
EP - 6148
BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Y2 - 15 April 2018 through 20 April 2018
ER -