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The task of dialog management is commonly decomposed into two sequentialsubtasks: dialog state tracking and dialog policy learning. In an end-to-enddialog system, the aim of dialog state tracking is to accurately estimate thetrue dialog state from noisy observations produced by the speech recognitionand the natural language understanding modules. The state tracking task isprimarily meant to support a dialog policy. From a probabilistic perspective,this is achieved by maintaining a posterior distribution over hidden dialogstates composed of a set of context dependent variables. Once a dialog policyis learned, it strives to select an optimal dialog act given the estimateddialog state and a defined reward function. This paper introduces a novelmethod of dialog state tracking based on a bilinear algebric decompositionmodel that provides an efficient inference schema through collective matrixfactorization. We evaluate the proposed approach on the second Dialog StateTracking Challenge (DSTC-2) dataset and we show that the proposed tracker givesencouraging results compared to the state-of-the-art trackers that participatedin this standard benchmark. Finally, we show that the prediction schema iscomputationally efficient in comparison to the previous approaches.

Spectral decomposition method of dialog state tracking via collective matrix factorization