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Numerous new dialog domains are being created every day while collecting datafor these domains is extremely costly since it involves human interactions.Therefore, it is essential to develop algorithms that can adapt to differentdomains efficiently when building data-driven dialog models. The most recentresearches on domain adaption focus on giving the model a betterinitialization, rather than optimizing the adaptation process. We propose anefficient domain adaptive task-oriented dialog system model, which incorporatesa meta-teacher model to emphasize the different impacts between generatedtokens with respect to the context. We first train our base dialog model andmeta-teacher model adversarially in a meta-learning setting on rich-resourcedomains. The meta-teacher learns to quantify the importance of tokens underdifferent contexts across different domains. During adaptation, themeta-teacher guides the dialog model to focus on important tokens in order toachieve better adaptation efficiency. We evaluate our model on two multi-domaindatasets, MultiWOZ and Google Schema-Guided Dialogue, and achievestate-of-the-art performance.

A Student-Teacher Architecture for Dialog Domain Adaptation under the Meta-Learning Setting