Deep Neural Network Approach for the Dialog State Tracking Challenge

Statistical dialog systems, in maintaining a distribution over multiple hypotheses of the true dialog state, are able to behave in a robust manner when faced with noisy conditions and ambiguity. Such systems rely on probabilistic tracking of dialog state, with improvements in the tracking quality being important in the system-wide performance in a dialog system (see e.g. Young et al. (2009)).

This paper presents a Deep Neural Network (DNN) approach for dialog state tracking which has been evaluated in the context of the Dialog State Tracking Challenge (DSTC) (Williams, 2012a; Williams et al., 2013)1.