Domain Adaptive Meta-Learning for Dialogue State Tracking

Abstract

Domain adaptation for low-resource dialogue state tracking (DST) is of significance due to the growing diversity of conversation scenarios. In this paper, we propose a novel domain adaptive model-agnostic meta-learning (DAMAML) framework. Under this framework, we equip the DST model with two domain adaptors and a unified parameter generator. The parameter generator takes a domain embedding as input to produce parameters of domain adaptors, which modulate domain-shared initial parameters to the subspace of each domain. In this way, we simultaneously model multiple individual meta-learners with each covering the distribution of one domain, allowing more efficient adaptation. Compared with the conventional MAML, this framework not only is able to seek domain-shared initial parameters that facilitate fast adaptation, but also has better capability to fit a diversified domain distribution. Experimental results and in-depth analysis demonstrate the effectiveness of the proposed framework.