  Few-shot NER needs to effectively capture information from limited instances
and transfer useful knowledge from external resources. In this paper, we
propose a self-describing mechanism for few-shot NER, which can effectively
leverage illustrative instances and precisely transfer knowledge from external
resources by describing both entity types and mentions using a universal
concept set. Specifically, we design Self-describing Networks (SDNet), a
Seq2Seq generation model which can universally describe mentions using
concepts, automatically map novel entity types to concepts, and adaptively
recognize entities on-demand. We pre-train SDNet with large-scale corpus, and
conduct experiments on 8 benchmarks from different domains. Experiments show
that SDNet achieves competitive performances on all benchmarks and achieves the
new state-of-the-art on 6 benchmarks, which demonstrates its effectiveness and
robustness.
