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    <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.</abstract>
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%0 Conference Proceedings
%T Meta-Reinforced Multi-Domain State Generator for Dialogue Systems
%A Huang, Yi
%A Feng, Junlan
%A Hu, Min
%A Wu, Xiaoting
%A Du, Xiaoyu
%A Ma, Shuo
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F huang-etal-2020-meta
%X 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.
%R 10.18653/v1/2020.acl-main.636
%U https://aclanthology.org/2020.acl-main.636
%U https://doi.org/10.18653/v1/2020.acl-main.636
%P 7109-7118

Markdown (Informal)

[Meta-Reinforced Multi-Domain State Generator for Dialogue Systems](https://aclanthology.org/2020.acl-main.636) (Huang et al., ACL 2020)

• Meta-Reinforced Multi-Domain State Generator for Dialogue Systems (Huang et al., ACL 2020)

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.