Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialog

Ryuichi Takanobu, Hanlin Zhu, Minlie Huang


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
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
Bibkey:
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)
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PDF:
https://aclanthology.org/D19-1010.pdf
Attachment:
 D19-1010.Attachment.zip
Code
 truthless11/GDPL