Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning
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| Authors | Kavosh Asadi, Jason D. Williams, Geoffrey Zweig |
| Journal/Conference Name | ACL 2017 7 |
| Paper Category | Artificial Intelligence |
| Paper Abstract | 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. |
| Date of publication | 2017 |
| Code Programming Language | Multiple |
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