  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.
