Prompt-based Text Entailment for Low-Resource Named Entity Recognition
This addresses the problem of limited labeled data for named entity recognition, offering a domain-specific improvement for low-resource NLP applications.
The paper tackles low-resource named entity recognition by reformulating it as a text entailment task using prompts with pre-trained language models, achieving competitive performance on CoNLL03 and outperforming fine-tuned methods on MIT Movie and Few-NERD datasets in low-resource settings.
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.