Using graphical models for mixed-initiative dialog management systems with realtime policies

In this paper, we present a novel approach for dialog modeling, which extends the idea underlying the partially observable Markov Decision Processes (POMDPs), i. e. it allows for calculating the dialog policy in real-time and thereby increases the system flexibility. The use of statistical dialog models is particularly advantageous to react adequately to common errors of speech recognition systems. Comparing our results to the reference system (POMDP), we achieve a relative reduction of 31:6% of the average dialog length. Furthermore, the proposed system shows a relative enhancement of 64:4% of the sensitivity rate in the error recognition capabilities using the same specifity rate in both systems. The achieved results are based on the Air Travelling Information System with 21 650 user utterances in 1 585 natural spoken dialogs.