{"id":238178,"date":"2016-04-01T00:00:00","date_gmt":"2016-04-01T07:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/the-dialog-state-tracking-challenge-series-a-review\/"},"modified":"2018-10-16T22:01:29","modified_gmt":"2018-10-17T05:01:29","slug":"the-dialog-state-tracking-challenge-series-a-review","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/the-dialog-state-tracking-challenge-series-a-review\/","title":{"rendered":"The Dialog State Tracking Challenge Series: A Review"},"content":{"rendered":"<div class=\"asset-content\">\n<p>In a spoken dialog system, <em>dialog state tracking<\/em> refers to the task of correctly inferring the state of the conversation \u2013 such as the user\u2019s goal \u2013 given all of the dialog history up to that turn. Dialog state tracking is crucial to the success of a dialog system, yet until recently there were no common resources, hampering progress. The <em>Dialog State Tracking Challenge<\/em> series of 3 tasks introduced the first shared testbed and evaluation metrics for dialog state tracking, and has underpinned three key advances in dialog state tracking: the move from generative to discriminative models; the adoption of discriminative sequential techniques; and the incorporation of the speech recognition results directly into the dialog state tracker. This paper reviews this research area, covering both the challenge tasks themselves and summarizing the work they have enabled.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In a spoken dialog system, dialog state tracking refers to the task of correctly inferring the state of the conversation \u2013 such as the user\u2019s goal \u2013 given all of the dialog history up to that turn. Dialog state tracking is crucial to the success of a dialog system, yet until recently there were no 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