  Zero-shot transfer learning for dialogue state tracking (DST) enables us to
handle a variety of task-oriented dialogue domains without the expense of
collecting in-domain data. In this work, we propose to transfer the
\textit{cross-task} knowledge from general question answering (QA) corpora for
the zero-shot DST task. Specifically, we propose TransferQA, a transferable
generative QA model that seamlessly combines extractive QA and multi-choice QA
via a text-to-text transformer framework, and tracks both categorical slots and
non-categorical slots in DST. In addition, we introduce two effective ways to
construct unanswerable questions, namely, negative question sampling and
context truncation, which enable our model to handle "none" value slots in the
zero-shot DST setting. The extensive experiments show that our approaches
substantially improve the existing zero-shot and few-shot results on MultiWoz.
Moreover, compared to the fully trained baseline on the Schema-Guided Dialogue
dataset, our approach shows better generalization ability in unseen domains.
