In task-oriented dialogue scenarios, cross-domain zero-shot slot filling plays a vital role in leveraging source domain knowledge to learn a model with high generalization ability in unknown target domain where annotated data is unavailable. However, the existing state-of-the-art zero-shot slot filling methods have limited generalization ability in target domain, they only show effective knowledge transfer on seen slots and perform poorly on unseen slots. To alleviate this issue, we present a novel Hierarchical Contrastive Learning Framework (HiCL) for zero-shot slot filling. Specifically, we propose a coarse- to fine-grained contrastive learning based on Gaussian-distributed embedding to learn the generalized deep semantic relations between utterance-tokens, by optimizing inter- and intra-token distribution distance. This encourages HiCL to generalize to the slot types unseen at training phase. Furthermore, we present a new iterative label set semantics inference method to unbiasedly and separately evaluate the performance of unseen slot types which entangled with their counterparts (i.e., seen slot types) in the previous zero-shot slot filling evaluation methods. The extensive empirical experiments on four datasets demonstrate that the proposed method achieves comparable or even better performance than the current state-of-the-art zero-shot slot filling approaches.

翻译：在面向任务的对话场景中，跨域零样本槽位填充通过利用源域知识来训练模型，使其在缺乏标注数据的未知目标域中具备高泛化能力，扮演着关键角色。然而，现有最先进的零样本槽位填充方法在目标域的泛化能力有限，仅在已见槽位类型上展现出有效的知识迁移，而对未见槽位类型的处理效果不佳。为解决此问题，我们提出了一种新颖的层次化对比学习框架（HiCL）用于零样本槽位填充。具体而言，我们提出了一种基于高斯分布嵌入的由粗到细的粒度对比学习方法，通过优化词元间分布距离与词元内分布距离，学习话语词元间的通用深层语义关系。这促使HiCL能够泛化到训练阶段未出现的槽位类型。此外，我们提出了一种新的迭代式标签集语义推断方法，用于无偏且独立地评估未见槽位类型的性能——此类类型在以往的零样本槽位填充评估方法中常与其对应类型（即已见槽位类型）混杂在一起。在四个数据集上的大量实证实验表明，所提方法达到了与当前最先进的零样本槽位填充方法相当甚至更优的性能。