  In this paper, we explore the slot tagging with only a few labeled support
sentences (a.k.a. few-shot). Few-shot slot tagging faces a unique challenge
compared to the other few-shot classification problems as it calls for modeling
the dependencies between labels. But it is hard to apply previously learned
label dependencies to an unseen domain, due to the discrepancy of label sets.
To tackle this, we introduce a collapsed dependency transfer mechanism into the
conditional random field (CRF) to transfer abstract label dependency patterns
as transition scores. In the few-shot setting, the emission score of CRF can be
calculated as a word's similarity to the representation of each label. To
calculate such similarity, we propose a Label-enhanced Task-Adaptive Projection
Network (L-TapNet) based on the state-of-the-art few-shot classification model
-- TapNet, by leveraging label name semantics in representing labels.
Experimental results show that our model significantly outperforms the
strongest few-shot learning baseline by 14.64 F1 scores in the one-shot
setting.
