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We present our submission to the End-to-End Multi-Domain Dialog ChallengeTrack of the Eighth Dialog System Technology Challenge. Our proposed dialogsystem adopts a pipeline architecture, with distinct components for NaturalLanguage Understanding, Dialog State Tracking, Dialog Management and NaturalLanguage Generation. At the core of our system is a reinforcement learningalgorithm which uses Deep Q-learning from Demonstrations to learn a dialogpolicy with the help of expert examples. We find that demonstrations areessential to training an accurate dialog policy where both state and actionspaces are large. Evaluation of our Dialog Management component shows that ourapproach is effective - beating supervised and reinforcement learningbaselines.

Show Us the Way: Learning to Manage Dialog from Demonstrations