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.

本论文提出一种增强型随机生成方法，利用自我注意力编码器对对话和插槽进行编码，然后以自回归的方式生成插槽值，并融入信息捕捉模板，实现跨领域知识转移，实验证明该方法在多领域体系下显著提高了当前的零样本跨领域任务对话状态跟踪技术。