Julien Perez
Published in the Special Issue on Dialog State Tracking, Dialogue & Discourse (D&D) journal 7 (3) (2016) 34-46.
The task of dialog management is commonly decomposed into two sequential subtasks: dialog
state tracking and dialog policy learning. In an end-to-end dialog system, the aim of dialog
state tracking is to accurately estimate the true dialog state from noisy observations produced by
the speech recognition and the natural language understanding modules. The state tracking task
is primarily meant to support a dialog policy. From a probabilistic perspective, this is achieved
by maintaining a posterior distribution over hidden dialog states composed of a set of context dependent
variables. Once a dialog policy is learned, it strives to select an optimal dialog act given
the estimated dialog state and a defined reward function. This paper introduces a novel method of
dialog state tracking based on a bilinear algebric decomposition model that provides an efficient inference
schema through collective matrix factorization. We evaluate the proposed approach on the
second Dialog State Tracking Challenge (DSTC-2) dataset and we show that the proposed tracker
gives encouraging results compared to the state-of-the-art trackers that participated in this standard
benchmark. Finally, we show that the prediction schema is computationally efficient in comparison to the previous approaches.
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