Abstract
This paper presents a new approach that extends Deep Dyna-Q (DDQ) by incorporating a Budget-Conscious Scheduling (BCS) to best utilize a fixed, small amount of user interactions (budget) for learning task-oriented dialogue agents. BCS consists of (1) a Poisson-based global scheduler to allocate budget over different stages of training; (2) a controller to decide at each training step whether the agent is trained using real or simulated experiences; (3) a user goal sampling module to generate the experiences that are most effective for policy learning. Experiments on a movie-ticket booking task with simulated and real users show that our approach leads to significant improvements in success rate over the state-of-the-art baselines given the fixed budget.- Anthology ID:
- P19-1364
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3742–3751
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/P19-1364/
- DOI:
- 10.18653/v1/P19-1364
- Cite (ACL):
- Zhirui Zhang, Xiujun Li, Jianfeng Gao, and Enhong Chen. 2019. Budgeted Policy Learning for Task-Oriented Dialogue Systems. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3742–3751, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Budgeted Policy Learning for Task-Oriented Dialogue Systems (Zhang et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/P19-1364.pdf