Abstract
This is a demonstration of interactive teaching for practical end-to-end dialog systems driven by a recurrent neural network. In this approach, a developer teaches the network by interacting with the system and providing on-the-spot corrections. Once a system is deployed, a developer can also correct mistakes in logged dialogs. This demonstration shows both of these teaching methods applied to dialog systems in three domains: pizza ordering, restaurant information, and weather forecasts.- Anthology ID:
- W17-5511
- Volume:
- Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue
- Month:
- August
- Year:
- 2017
- Address:
- Saarbrücken, Germany
- Editors:
- Kristiina Jokinen, Manfred Stede, David DeVault, Annie Louis
- Venue:
- SIGDIAL
- SIG:
- SIGDIAL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 82–85
- Language:
- URL:
- https://aclanthology.org/W17-5511
- DOI:
- 10.18653/v1/W17-5511
- Cite (ACL):
- Jason D. Williams and Lars Liden. 2017. Demonstration of interactive teaching for end-to-end dialog control with hybrid code networks. In Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, pages 82–85, Saarbrücken, Germany. Association for Computational Linguistics.
- Cite (Informal):
- Demonstration of interactive teaching for end-to-end dialog control with hybrid code networks (Williams & Liden, SIGDIAL 2017)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/W17-5511.pdf