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In spoken dialog systems, statistical state tracking aims to improve robustness to speech recognition errors by tracking a posterior distribution over hidden dialog states. This paper introduces two novel methods for this task. First, we explain how state tracking is structurally simi-lar to web-style ranking, enabling ma-ture, powerful ranking algorithms to be ap-plied. Second, we show how to use mul-tiple spoken language understanding en-gines (SLUs) in state tracking — multiple SLUs can expand the set of dialog states being tracked, and give more information about each, thereby increasing both recall and precision of state tracking. We eval-uate on the second Dialog State Tracking Challenge; together these two techniques yield highest accuracy in 2 of 3 tasks, in-cluding the most difficult and general task. 1
J. D. Williams (Wed,) studied this question.