The use of discriminative belief tracking in POMDP-based dialogue systems

Statistical spoken dialogue systems based on Partially Observable Markov Decision Processes (POMDPs) have been shown to be more robust to speech recognition errors by maintaining a belief distribution over multiple dialogue states and making policy decisions based on the entire distribution rather than the single most likely hypothesis. To date most POMDP-based systems have used generative trackers. However, concerns about modelling accuracy have created interest in discriminative methods, and recent results from the second Dialog State Tracking Challenge (DSTC2) have shown that discriminative trackers can significantly outperform generative models in terms of tracking accuracy. The aim of this paper is to investigate the extent to which these improvements translate into improved task completion rates when incorporated into a spoken dialogue system. To do this, the Recurrent Neural Network (RNN) tracker described by Henderson et al in DSTC2 was integrated into the Cambridge statistical dialogue system and compared with the existing generative Bayesian network tracker. Using a Gaussian Process (GP) based policy, the experimental results indicate that the system using the RNN tracker performs significantly better than the system with the original Bayesian network tracker.