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.

在任务导向的对话场景中，跨领域零样本槽填充在利用源领域知识学习高泛化能力模型的同时，在未标注数据的未知目标领域中起到至关重要的作用。然而，现有最先进的零样本槽填充方法在目标领域中的泛化能力有限，只对已见槽显示有效的知识转移，在未见槽上表现不佳。为了缓解这一问题，我们提出了一种新颖的分层对比学习框架（HiCL）用于零样本槽填充。具体而言，我们提出了一种基于高斯分布嵌入的粗细粒度对比学习方法，以学习语句标记之间的广义深层语义关系，通过优化之间和之内的标记分布距离。这促使 HiCL 在训练阶段泛化到未见的槽类型上。此外，我们提出了一种新的迭代标签集语义推断方法，可以不偏不倚地分别评估在先前的零样本槽填充评估方法中与它们的对应槽类型相纠缠的未见槽类型的性能。对四个数据集的广泛经验性实验证明，所提出的方法的性能与当前最先进的零样本槽填充方法相当甚至更好。