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：在任务导向型对话场景中，跨域零样本槽填充通过利用源域知识，使模型在缺乏标注数据的未知目标域中具备高泛化能力，发挥关键作用。然而，现有最先进的零样本槽填充方法在目标域泛化能力有限，仅对已见槽类型实现有效知识迁移，而在未见槽类型上表现不佳。为解决该问题，我们提出一种新颖的层级对比学习框架（HiCL）用于零样本槽填充。具体而言，我们提出基于高斯分布嵌入的粗到细粒度对比学习，通过优化词元间与词元内分布距离，学习语句-词元间的广义深层语义关系。该方法促使HiCL向训练阶段未出现的槽类型进行泛化。此外，我们提出一种新的迭代式标签集语义推断方法，以无偏且独立的方式评估未见槽类型的性能——此前零样本槽填充评估方法将未见槽与其对应类型（即已见槽类型）纠缠建模。在四个数据集上的大量实证实验表明，所提方法性能达到甚至超越当前最先进的零样本槽填充方法。