How to Build User Simulators to Train RL-based Dialog Systems

Weiyan Shi, Kun Qian, Xuewei Wang, Zhou Yu


Abstract
User simulators are essential for training reinforcement learning (RL) based dialog models. The performance of the simulator directly impacts the RL policy. However, building a good user simulator that models real user behaviors is challenging. We propose a method of standardizing user simulator building that can be used by the community to compare dialog system quality using the same set of user simulators fairly. We present implementations of six user simulators trained with different dialog planning and generation methods. We then calculate a set of automatic metrics to evaluate the quality of these simulators both directly and indirectly. We also ask human users to assess the simulators directly and indirectly by rating the simulated dialogs and interacting with the trained systems. This paper presents a comprehensive evaluation framework for user simulator study and provides a better understanding of the pros and cons of different user simulators, as well as their impacts on the trained systems.
Anthology ID:
D19-1206
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:
1990–2000
Language:
URL:
https://aclanthology.org/D19-1206
DOI:
10.18653/v1/D19-1206
Bibkey:
Cite (ACL):
Weiyan Shi, Kun Qian, Xuewei Wang, and Zhou Yu. 2019. How to Build User Simulators to Train RL-based Dialog Systems. 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 1990–2000, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
How to Build User Simulators to Train RL-based Dialog Systems (Shi et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/D19-1206.pdf
Attachment:
 D19-1206.Attachment.pdf
Code
 wyshi/user-simulator
Data
MultiWOZ