  Few-shot named entity recognition (NER) exploits limited annotated instances
to identify named mentions. Effectively transferring the internal or external
resources thus becomes the key to few-shot NER. While the existing prompt
tuning methods have shown remarkable few-shot performances, they still fail to
make full use of knowledge. In this work, we investigate the integration of
rich knowledge to prompt tuning for stronger few-shot NER. We propose
incorporating the deep prompt tuning framework with threefold knowledge (namely
TKDP), including the internal 1) context knowledge and the external 2) label
knowledge & 3) sememe knowledge. TKDP encodes the three feature sources and
incorporates them into the soft prompt embeddings, which are further injected
into an existing pre-trained language model to facilitate predictions. On five
benchmark datasets, our knowledge-enriched model boosts by at most 11.53% F1
over the raw deep prompt method, and significantly outperforms 8
strong-performing baseline systems in 5-/10-/20-shot settings, showing great
potential in few-shot NER. Our TKDP can be broadly adapted to other few-shot
tasks without effort.
