  Few-shot named entity recognition (NER) targets generalizing to unseen labels
and/or domains with few labeled examples. Existing metric learning methods
compute token-level similarities between query and support sets, but are not
able to fully incorporate label semantics into modeling. To address this issue,
we propose a simple method to largely improve metric learning for NER: 1)
multiple prompt schemas are designed to enhance label semantics; 2) we propose
a novel architecture to effectively combine multiple prompt-based
representations. Empirically, our method achieves new state-of-the-art (SOTA)
results under 16 of the 18 considered settings, substantially outperforming the
previous SOTA by an average of 8.84% and a maximum of 34.51% in relative gains
of micro F1. Our code is available at https://github.com/AChen-qaq/ProML.
