  Knowledge (including structured knowledge such as schema and ontology, and
unstructured knowledge such as web corpus) is a critical part of dialog
understanding, especially for unseen tasks and domains. Traditionally, such
domain-specific knowledge is encoded implicitly into model parameters for the
execution of downstream tasks, which makes training inefficient. In addition,
such models are not easily transferable to new tasks with different schemas. In
this work, we propose to perform dialog state tracking grounded on knowledge
encoded externally. We query relevant knowledge of various forms based on the
dialog context where such information can ground the prediction of dialog
states. We demonstrate superior performance of our proposed method over strong
baselines, especially in the few-shot learning setting.
