  Numerous new dialog domains are being created every day while collecting data
for these domains is extremely costly since it involves human interactions.
Therefore, it is essential to develop algorithms that can adapt to different
domains efficiently when building data-driven dialog models. The most recent
researches on domain adaption focus on giving the model a better
initialization, rather than optimizing the adaptation process. We propose an
efficient domain adaptive task-oriented dialog system model, which incorporates
a meta-teacher model to emphasize the different impacts between generated
tokens with respect to the context. We first train our base dialog model and
meta-teacher model adversarially in a meta-learning setting on rich-resource
domains. The meta-teacher learns to quantify the importance of tokens under
different contexts across different domains. During adaptation, the
meta-teacher guides the dialog model to focus on important tokens in order to
achieve better adaptation efficiency. We evaluate our model on two multi-domain
datasets, MultiWOZ and Google Schema-Guided Dialogue, and achieve
state-of-the-art performance.
