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

End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning