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Few-shot Named Entity Recognition with Self-describing Networks

2022-03-23 07:56:27
Jiawei Chen, Qing Liu, Hongyu Lin, Xianpei Han, Le Sun

Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2203.12252

PDF

https://arxiv.org/pdf/2203.12252.pdf


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