Extrinsic Evaluation of Dialog State Tracking and Predictive Metrics for Dialog Policy Optimization

During the recent Dialog State Tracking Challenge (DSTC), a fundamental question was raised: “Would better performance in dialog state tracking translate to better performance of the optimized policy by reinforcement learning?” Also, during the challenge system evaluation, another nontrivial question arose: “Which evaluation metric and schedule would best predict improvement in overall dialog performance?” This paper aims to answer these questions by applying an off-policy reinforcement learning method to the output of each challenge system. The results give a positive answer to the first question. Thus the effort to separately improve the performance of dialog state tracking as carried out in the DSTC may be justified. The answer to the second question also draws several insightful conclusions on the characteristics of different evaluation metrics and schedules.