  End-to-end learning of recurrent neural networks (RNNs) is an attractive
solution for dialog systems; however, current techniques are data-intensive and
require thousands of dialogs to learn simple behaviors. We introduce Hybrid
Code Networks (HCNs), which combine an RNN with domain-specific knowledge
encoded as software and system action templates. Compared to existing
end-to-end approaches, HCNs considerably reduce the amount of training data
required, while retaining the key benefit of inferring a latent representation
of dialog state. In addition, HCNs can be optimized with supervised learning,
reinforcement learning, or a mixture of both. HCNs attain state-of-the-art
performance on the bAbI dialog dataset, and outperform two commercially
deployed customer-facing dialog systems.
