Few-shot Named Entity Recognition with Self-describing Networks
Jiawei Chen, Qing Liu, Hongyu Lin, Xianpei Han, Le Sun · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2022
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 ondemand.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.