Learning Meta-Prompt with Entity-Enhanced Semantics for Few-Shot Named Entity Recognition

Abstract

Recently, prompt-tuning has been proved to be surprisingly effective on few-shot tasks. Intuitively, some studies explore Few-shot Named Entity Recognition (NER) based on prompt-tuning. However, how to properly initialize and effectively learn the prompt under limited training conditions still remains significantly challenging for few-shot NER. To meet these challenges, we propose a novel Meta-Prompt with Entity-Enhanced semantics for Few-shot N}R, MPE^3 for brevity. Specifically, we first explore the importance of the named entities' semantics in the few-shot NER task. And we propose to construct prompts with entity-enhanced semantics which contain much useful prior knowledge for identifying named entities. Furthermore, to address the issue of inadequate training more substantially, we aim to train a meta-prompt that can be more effective and adaptive for few-shot NER scenarios. To achieve this, we divide the training data into many source-domain agnostic meta-tasks tailored to the characteristics of the NER problem for training. And we specially design a prompt meta-learner for training these meta-tasks. This training strategy succeeds in guiding prompts to optimize in a better direction for few-shot scenarios by the learned meta-knowledge from each meta-task. We conduct extensive experiments on three NER datasets under two different few-shot settings. Our method outperforms the current state-of-the-art model by 5.60%~13.34% and 2.41%~7.34% on average in the two different few-shot settings respectively, which validates the effectiveness and superiority of our model.

Keywords: Information Extraction, Few-shot Named Entity Recognition, Prompt-Learning, Meta-Learning