Meta-Reinforced Multi-Domain State Generator for Dialogue Systems
Yi Huang, Junlan Feng, Min Hu, Xiaoting Wu, Xiaoyu Du, Shuo Ma
Abstract
A Dialogue State Tracker (DST) is a core component of a modular task-oriented dialogue system. Tremendous progress has been made in recent years. However, the major challenges remain. The state-of-the-art accuracy for DST is below 50% for a multi-domain dialogue task. A learnable DST for any new domain requires a large amount of labeled in-domain data and training from scratch. In this paper, we propose a Meta-Reinforced Multi-Domain State Generator (MERET). Our first contribution is to improve the DST accuracy. We enhance a neural model based DST generator with a reward manager, which is built on policy gradient reinforcement learning (RL) to fine-tune the generator. With this change, we are able to improve the joint accuracy of DST from 48.79% to 50.91% on the MultiWOZ corpus. Second, we explore to train a DST meta-learning model with a few domains as source domains and a new domain as target domain. We apply the model-agnostic meta-learning algorithm (MAML) to DST and the obtained meta-learning model is used for new domain adaptation. Our experimental results show this solution is able to outperform the traditional training approach with extremely less training data in target domain.- Anthology ID:
- 2020.acl-main.636
- 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:
- 7109–7118
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.636
- DOI:
- 10.18653/v1/2020.acl-main.636
- Cite (ACL):
- Yi Huang, Junlan Feng, Min Hu, Xiaoting Wu, Xiaoyu Du, and Shuo Ma. 2020. Meta-Reinforced Multi-Domain State Generator for Dialogue Systems. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7109–7118, Online. Association for Computational Linguistics.
- Cite (Informal):
- Meta-Reinforced Multi-Domain State Generator for Dialogue Systems (Huang et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.acl-main.636.pdf