  Dialog State Tracking (DST), an integral part of modern dialog systems, aims
to track user preferences and constraints (slots) in task-oriented dialogs. In
real-world settings with constantly changing services, DST systems must
generalize to new domains and unseen slot types. Existing methods for DST do
not generalize well to new slot names and many require known ontologies of slot
types and values for inference. We introduce a novel ontology-free framework
that supports natural language queries for unseen constraints and slots in
multi-domain task-oriented dialogs. Our approach is based on generative
question-answering using a conditional language model pre-trained on
substantive English sentences. Our model improves joint goal accuracy in
zero-shot domain adaptation settings by up to 9% (absolute) over the previous
state-of-the-art on the MultiWOZ 2.1 dataset.
