  Zero-shot cross-domain slot filling alleviates the data dependence in the
case of data scarcity in the target domain, which has aroused extensive
research. However, as most of the existing methods do not achieve effective
knowledge transfer to the target domain, they just fit the distribution of the
seen slot and show poor performance on unseen slot in the target domain. To
solve this, we propose a novel approach based on prototypical contrastive
learning with a dynamic label confusion strategy for zero-shot slot filling.
The prototypical contrastive learning aims to reconstruct the semantic
constraints of labels, and we introduce the label confusion strategy to
establish the label dependence between the source domains and the target domain
on-the-fly. Experimental results show that our model achieves significant
improvement on the unseen slots, while also set new state-of-the-arts on slot
filling task.

Computation and Language