The Dialog State Tracking Challenge with Bayesian Approach
  Generative model has been one of the most common approaches for solving the
Dialog State Tracking Problem with the capabilities to model the dialog
hypotheses in an explicit manner. The most important task in such Bayesian
networks models is constructing the most reliable user models by learning and
reflecting the training data into the probability distribution of user actions
conditional on networks states. This paper provides an overall picture of the
learning process in a Bayesian framework with an emphasize on the
state-of-the-art theoretical analyses of the Expectation Maximization learning
algorithm.
