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This paper examines two statistical spoken dialog systems deployed to the public, extending an earlier study on one system . Results across the two systems show that statistical techniques improved performance in some cases, but degraded performance in others. Investigating degradations, we find the three main causes are (non-obviously) inaccurate parameter estimates, poor confidence scores, and correlations in speech recognition errors. We also find evidence for fundamental weaknesses in the formulation of the model as a generative process, and briefly show the potential of a discriminatively-trained alternative.