  We present our submission to the End-to-End Multi-Domain Dialog Challenge
Track of the Eighth Dialog System Technology Challenge. Our proposed dialog
system adopts a pipeline architecture, with distinct components for Natural
Language Understanding, Dialog State Tracking, Dialog Management and Natural
Language Generation. At the core of our system is a reinforcement learning
algorithm which uses Deep Q-learning from Demonstrations to learn a dialog
policy with the help of expert examples. We find that demonstrations are
essential to training an accurate dialog policy where both state and action
spaces are large. Evaluation of our Dialog Management component shows that our
approach is effective - beating supervised and reinforcement learning
baselines.
