Few-Shot Sequence Labeling with Label Dependency Transfer and Pair-wise Embedding

Understand

While few-shot classification has been widely explored with similarity based methods, few-shot sequence labeling poses a unique challenge as it also calls for modeling the label dependencies.

• To consider both the item similarity and label dependency, we propose to leverage the conditional random fields (CRFs) in few-shot sequence labeling.
• It calculates emission score with similarity based methods and obtains transition score with a specially designed transfer mechanism.
• When applying CRF in the few-shot scenarios, the discrepancy of label sets among different domains makes it hard to use the label dependency learned in prior domains.