Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning
This addresses the challenge of building efficient dialog systems for applications like customer service, though it is incremental as it builds on existing end-to-end methods.
The paper tackles the problem of data-intensive training for end-to-end dialog systems by introducing Hybrid Code Networks (HCNs), which combine RNNs with domain-specific knowledge to reduce training data requirements while achieving state-of-the-art performance on the bAbI dataset and outperforming commercial systems.
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.