Learning In-context Learning for Named Entity Recognition
This addresses the challenge of diverse and emerging entity types with limited annotations for NER tasks, representing an incremental advancement in adapting PLMs for few-shot learning.
The paper tackles the problem of named entity recognition (NER) in real-world applications by proposing an in-context learning-based approach that injects NER ability into pre-trained language models (PLMs) to recognize entities of novel types using few demonstrations, achieving significant performance improvements over fine-tuning counterparts on 4 few-shot NER datasets.
Named entity recognition in real-world applications suffers from the diversity of entity types, the emergence of new entity types, and the lack of high-quality annotations. To address the above problems, this paper proposes an in-context learning-based NER approach, which can effectively inject in-context NER ability into PLMs and recognize entities of novel types on-the-fly using only a few demonstrative instances. Specifically, we model PLMs as a meta-function $\mathcal{ λ_ {\text{instruction, demonstrations, text}}. M}$, and a new entity extractor can be implicitly constructed by applying new instruction and demonstrations to PLMs, i.e., $\mathcal{ (λ. M) }$(instruction, demonstrations) $\to$ $\mathcal{F}$ where $\mathcal{F}$ will be a new entity extractor, i.e., $\mathcal{F}$: text $\to$ entities. To inject the above in-context NER ability into PLMs, we propose a meta-function pre-training algorithm, which pre-trains PLMs by comparing the (instruction, demonstration)-initialized extractor with a surrogate golden extractor. Experimental results on 4 few-shot NER datasets show that our method can effectively inject in-context NER ability into PLMs and significantly outperforms the PLMs+fine-tuning counterparts.