Deep Neural Network Approach for the Dialog State Tracking Challenge

While belief tracking is known to be important in allowing statistical dialog systems to manage dialogs in a highly robust manner, until recently little attention has been given to analysing the behaviour of belief tracking techniques. The Dialogue State Tracking Challenge has allowed for such an analysis, comparing multiple belief tracking approaches on a shared task. Recent success in using deep learning for speech research motivates the Deep Neural Network approach presented here. The model parameters can be learnt by directly maximising the likelihood of the training data. The paper explores some aspects of the training, and the resulting tracker is found to perform competitively, particularly on a corpus of dialogs from a system not found in the training.