Few-shot Named Entity Recognition with Self-describing Networks
This addresses the problem of limited labeled data for named entity recognition in various domains, representing a novel method rather than an incremental improvement.
The paper tackles few-shot named entity recognition by proposing a self-describing mechanism that leverages illustrative instances and transfers knowledge from external resources using a universal concept set, achieving state-of-the-art results on 6 out of 8 benchmarks.
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.