  Zero-shot cross-domain dialogue state tracking (DST) enables us to handle
task-oriented dialogue in unseen domains without the expense of collecting
in-domain data. In this paper, we propose a slot description enhanced
generative approach for zero-shot cross-domain DST. Specifically, our model
first encodes dialogue context and slots with a pre-trained self-attentive
encoder, and generates slot values in an auto-regressive manner. In addition,
we incorporate Slot Type Informed Descriptions that capture the shared
information across slots to facilitate cross-domain knowledge transfer.
Experimental results on the MultiWOZ dataset show that our proposed method
significantly improves existing state-of-the-art results in the zero-shot
cross-domain setting.
