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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 (Bordes and Weston, 2016), and outperform two commercially deployed customer-facing dialog systems at our company.</abstract>
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%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

Markdown (Informal)

[Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning](https://aclanthology.org/P17-1062) (Williams et al., ACL 2017)

• Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning (Williams et al., ACL 2017)

ACL

• Jason D. Williams, Kavosh Asadi, and Geoffrey Zweig. 2017. Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 665–677, Vancouver, Canada. Association for Computational Linguistics.