  Named Entity Recognition (NER) in Few-Shot setting is imperative for entity
tagging in low resource domains. Existing approaches only learn class-specific
semantic features and intermediate representations from source domains. This
affects generalizability to unseen target domains, resulting in suboptimal
performances. To this end, we present CONTaiNER, a novel contrastive learning
technique that optimizes the inter-token distribution distance for Few-Shot
NER. Instead of optimizing class-specific attributes, CONTaiNER optimizes a
generalized objective of differentiating between token categories based on
their Gaussian-distributed embeddings. This effectively alleviates overfitting
issues originating from training domains. Our experiments in several
traditional test domains (OntoNotes, CoNLL'03, WNUT '17, GUM) and a new large
scale Few-Shot NER dataset (Few-NERD) demonstrate that on average, CONTaiNER
outperforms previous methods by 3%-13% absolute F1 points while showing
consistent performance trends, even in challenging scenarios where previous
approaches could not achieve appreciable performance.
