%0 Conference Proceedings %T Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning %A Williams, Jason D. %A Asadi, Kavosh %A Zweig, Geoffrey %Y Barzilay, Regina %Y Kan, Min-Yen %S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) %D 2017 %8 July %I Association for Computational Linguistics %C Vancouver, Canada %F williams-etal-2017-hybrid %X 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 (Bordes and Weston, 2016), and outperform two commercially deployed customer-facing dialog systems at our company. %R 10.18653/v1/P17-1062 %U https://aclanthology.org/P17-1062 %U https://doi.org/10.18653/v1/P17-1062 %P 665-677