  This paper presents a model for end-to-end learning of task-oriented dialog
systems. The main component of the model is a recurrent neural network (an
LSTM), which maps from raw dialog history directly to a distribution over
system actions. The LSTM automatically infers a representation of dialog
history, which relieves the system developer of much of the manual feature
engineering of dialog state. In addition, the developer can provide software
that expresses business rules and provides access to programmatic APIs,
enabling the LSTM to take actions in the real world on behalf of the user. The
LSTM can be optimized using supervised learning (SL), where a domain expert
provides example dialogs which the LSTM should imitate; or using reinforcement
learning (RL), where the system improves by interacting directly with end
users. Experiments show that SL and RL are complementary: SL alone can derive a
reasonable initial policy from a small number of training dialogs; and starting
RL optimization with a policy trained with SL substantially accelerates the
learning rate of RL.
