Abstract
Dialog policy decides what and how a task-oriented dialog system will respond, and plays a vital role in delivering effective conversations. Many studies apply Reinforcement Learning to learn a dialog policy with the reward function which requires elaborate design and pre-specified user goals. With the growing needs to handle complex goals across multiple domains, such manually designed reward functions are not affordable to deal with the complexity of real-world tasks. To this end, we propose Guided Dialog Policy Learning, a novel algorithm based on Adversarial Inverse Reinforcement Learning for joint reward estimation and policy optimization in multi-domain task-oriented dialog. The proposed approach estimates the reward signal and infers the user goal in the dialog sessions. The reward estimator evaluates the state-action pairs so that it can guide the dialog policy at each dialog turn. Extensive experiments on a multi-domain dialog dataset show that the dialog policy guided by the learned reward function achieves remarkably higher task success than state-of-the-art baselines.- Anthology ID:
- D19-1010
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 100–110
- Language:
- URL:
- https://aclanthology.org/D19-1010
- DOI:
- 10.18653/v1/D19-1010
- Cite (ACL):
- Ryuichi Takanobu, Hanlin Zhu, and Minlie Huang. 2019. Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialog. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 100–110, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialog (Takanobu et al., EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/D19-1010.pdf
- Code
- truthless11/GDPL