This track of the DSTC-6 challenge series is aiming to build End-to-End dialog systems for goal-oriented applications. Goal-oriented dialog technology is an important research issue and End-to-End dialog learning has emerged as a primary research subject in the domain of conversation agent learning. It consists in learning a dialog policy from transactional dialogs of a given domain. In this task, the automatic system responses generated using a given task-oriented dialog data will be evaluated.
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