Pre-trained Language Models (PLMs) have been applied in NLP tasks and achieve
promising results. Nevertheless, the fine-tuning procedure needs labeled data
of the target domain, making it difficult to learn in low-resource and
non-trivial labeled scenarios. To address these challenges, we propose
Prompt-based Text Entailment (PTE) for low-resource named entity recognition,
which better leverages knowledge in the PLMs. We first reformulate named entity
recognition as the text entailment task. The original sentence with entity
type-specific prompts is fed into PLMs to get entailment scores for each
candidate. The entity type with the top score is then selected as final label.
Then, we inject tagging labels into prompts and treat words as basic units
instead of n-gram spans to reduce time complexity in generating candidates by
n-grams enumeration. Experimental results demonstrate that the proposed method
PTE achieves competitive performance on the CoNLL03 dataset, and better than
fine-tuned counterparts on the MIT Movie and Few-NERD dataset in low-resource
settings.

本文提出了 Prompt-based Text Entailment (PTE) 低资源命名实体识别技术，将命名实体识别作为文本蕴含任务，并使用 Pre-trained Language Models 和 entity type-specific prompts 来获得高性能。通过在 CoNLL03 数据集和 MIT Movie 及 Few-NERD 数据集上进行的实验，结果表明该方法 PTE 在低资源场景下的表现优于微调的对照组。