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.- Anthology ID:
- P17-1062
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 665–677
- Language:
- URL:
- https://aclanthology.org/P17-1062
- DOI:
- 10.18653/v1/P17-1062
- Cite (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.
- Cite (Informal):
- Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning (Williams et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/P17-1062.pdf
- Code
- additional community code