Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning

ACL 2017

Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning

Jan 22, 2021
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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. Authors: Jason D. Williams, Kavosh Asadi, Geoffrey Zweig (Microsoft Research, Brown University)

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