  Few-Shot Sequence Labeling (FSSL) is a canonical paradigm for the tagging
models, e.g., named entity recognition and slot filling, to generalize on an
emerging, resource-scarce domain. Recently, the metric-based meta-learning
framework has been recognized as a promising approach for FSSL. However, most
prior works assign a label to each token based on the token-level similarities,
which ignores the integrality of named entities or slots. To this end, in this
paper, we propose ESD, an Enhanced Span-based Decomposition method for FSSL.
ESD formulates FSSL as a span-level matching problem between test query and
supporting instances. Specifically, ESD decomposes the span matching problem
into a series of span-level procedures, mainly including enhanced span
representation, class prototype aggregation and span conflicts resolution.
Extensive experiments show that ESD achieves the new state-of-the-art results
on two popular FSSL benchmarks, FewNERD and SNIPS, and is proven to be more
robust in the nested and noisy tagging scenarios. Our code is available at
https://github.com/Wangpeiyi9979/ESD.
