[4G1] [General Session] 9. NLP / IR

[4G1-01] Dialog Breakdown Detection using Dialog Model based on Quasi-Recurrent Neural Networks

〇Ryota Tanaka¹, Akinobu Lee¹ (1. Department of Computer Science and Engineering, Graduate School of Engineering, Nagoya Institute of Technology)

Keywords:Dialog Breakdown Detection, Dialog Model, Quasi-Recurrent Neural Networks

In recent years, studies on chat-oriented dialog systems have been actively conducted due to the spread of dialog agents. On the other hand, many chat-oriented dialog systems have frequent dialog breakdown in which dialog is not smoothly performed. To tackle this problem, we propose a method to perform fast learning and robust dialog breakdown detection using Dialog Model based on Quasi-Recurrent Neural Networks (QRNN). To clarify the effectiveness, we conducted comparison experiment with other Recurrent Neural Networks (RNN) models, and show that QRNN has a faster learning and more accurate dialog breakdown detection than RNN.