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.

翻译：零点跨域对话状态跟踪(DST) 使我们能够在不花费收集内域数据的情况下处理在无形领域面向任务的对话。 在本文中,我们建议对零点跨域 DST 采用一个更强化的基因化方法。 具体地说, 我们的模型首先将对话背景和位置编码成一个经过预先训练的自我识别编码器, 并以自动递减的方式生成空位值。 此外, 我们加入了Slot type Intellical Intal Intal Intriations, 记录跨空位共享的信息, 以促进跨空位知识的转移。 多功能区数据集的实验结果显示, 我们提出的方法大大改进了零点交叉域设置的现有最新结果 。