  Few-shot named entity recognition (NER) aims at identifying named entities
based on only few labeled instances. Current few-shot NER methods focus on
leveraging existing datasets in the rich-resource domains which might fail in a
training-from-scratch setting where no source-domain data is used. To tackle
training-from-scratch setting, it is crucial to make full use of the annotation
information (the boundaries and entity types). Therefore, in this paper, we
propose a novel multi-task (Seed, Expand and Entail) learning framework,
SEE-Few, for Few-shot NER without using source domain data. The seeding and
expanding modules are responsible for providing as accurate candidate spans as
possible for the entailing module. The entailing module reformulates span
classification as a textual entailment task, leveraging both the contextual
clues and entity type information. All the three modules share the same text
encoder and are jointly learned. Experimental results on four benchmark
datasets under the training-from-scratch setting show that the proposed method
outperformed state-of-the-art few-shot NER methods with a large margin. Our
code is available at https://github.com/unveiled-the-red-hat/SEE-Few.
