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Few-shot named entity recognition (NER) aims at identifying named entitiesbased on only few labeled instances. Current few-shot NER methods focus onleveraging existing datasets in the rich-resource domains which might fail in atraining-from-scratch setting where no source-domain data is used. To tackletraining-from-scratch setting, it is crucial to make full use of the annotationinformation (the boundaries and entity types). Therefore, in this paper, wepropose a novel multi-task (Seed, Expand and Entail) learning framework,SEE-Few, for Few-shot NER without using source domain data. The seeding andexpanding modules are responsible for providing as accurate candidate spans aspossible for the entailing module. The entailing module reformulates spanclassification as a textual entailment task, leveraging both the contextualclues and entity type information. All the three modules share the same textencoder and are jointly learned. Experimental results on four benchmarkdatasets under the training-from-scratch setting show that the proposed methodoutperformed state-of-the-art few-shot NER methods with a large margin. Ourcode is available at https://github.com/unveiled-the-red-hat/SEE-Few.

SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition