Decomposed Two-Stage Prompt Learning for Few-Shot Named Entity Recognition

Abstract

Named entity recognition (NER) in a few-shot setting is an extremely challenging task, and most existing methods fail to account for the gap between NER tasks and pre-trained language models. Although prompt learning has been successfully applied in few-shot classification tasks, adapting to token-level classification similar to the NER task presents challenges in terms of time consumption and efficiency. In this work, we propose a decomposed prompt learning NER framework for few-shot settings, decomposing the NER task into two stages: entity locating and entity typing. In training, the location information of distant labels is used to train the entity locating model. A concise but effective prompt template is built to train the entity typing model. In inference, a pipeline approach is used to handle the entire NER task, which elegantly resolves time-consuming and inefficient problems. Specifically, a well-trained entity locating model is used to predict entity spans for each input. The input is then transformed using prompt templates, and the well-trained entity typing model is used to predict their types in a single step. Experimental results demonstrate that our framework outperforms previous prompt-based methods by an average of 2.3–12.9% in F1 score while achieving the best trade-off between accuracy and inference speed.