During the recent Dialog State Tracking\u00a0Challenge (DSTC), a fundamental question\u00a0was raised: \u201cWould better performance in\u00a0dialog state tracking translate to better\u00a0performance of the optimized policy by\u00a0reinforcement learning?\u201d Also, during the\u00a0challenge system evaluation, another nontrivial\u00a0question arose: \u201cWhich evaluation\u00a0metric and schedule would best predict\u00a0improvement in overall dialog performance?\u201d\u00a0This paper aims to answer these questions by\u00a0applying an off-policy reinforcement learning\u00a0method to the output of each challenge system.\u00a0The results give a positive answer to the first\u00a0question. Thus the effort to separately improve\u00a0the performance of dialog state tracking as\u00a0carried out in the DSTC may be justified. The\u00a0answer to the second question also draws\u00a0several insightful conclusions on the\u00a0characteristics of different evaluation metrics\u00a0and schedules.<\/p>\n","protected":false},"excerpt":{"rendered":"
During the recent Dialog State Tracking\u00a0Challenge (DSTC), a fundamental question\u00a0was raised: \u201cWould better performance in\u00a0dialog state tracking translate to better\u00a0performance of the optimized policy by\u00a0reinforcement learning?\u201d Also, during the\u00a0challenge system evaluation, another nontrivial\u00a0question arose: \u201cWhich evaluation\u00a0metric and schedule would best predict\u00a0improvement in overall dialog performance?\u201d\u00a0This paper aims to answer these questions by\u00a0applying an off-policy reinforcement […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"sule","user_id":"36377"}],"msr_publishername":"Association for Computational 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