Abstract
Deep reinforcement learning is a promising approach to training a dialog manager, but current methods struggle with the large state and action spaces of multi-domain dialog systems. Building upon Deep Q-learning from Demonstrations (DQfD), an algorithm that scores highly in difficult Atari games, we leverage dialog data to guide the agent to successfully respond to a user’s requests. We make progressively fewer assumptions about the data needed, using labeled, reduced-labeled, and even unlabeled data to train expert demonstrators. We introduce Reinforced Fine-tune Learning, an extension to DQfD, enabling us to overcome the domain gap between the datasets and the environment. Experiments in a challenging multi-domain dialog system framework validate our approaches, and get high success rates even when trained on out-of-domain data.- Anthology ID:
- 2020.acl-main.129
- Original:
- 2020.acl-main.129v1
- Version 2:
- 2020.acl-main.129v2
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1394–1405
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.129
- DOI:
- 10.18653/v1/2020.acl-main.129
- Cite (ACL):
- Gabriel Gordon-Hall, Philip John Gorinski, and Shay B. Cohen. 2020. Learning Dialog Policies from Weak Demonstrations. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1394–1405, Online. Association for Computational Linguistics.
- Cite (Informal):
- Learning Dialog Policies from Weak Demonstrations (Gordon-Hall et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.acl-main.129.pdf
- Data
- MultiWOZ