论文标题
Qaner：提示问题回答几个命名实体识别的模型
QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition
论文作者
论文摘要
最近，通过利用提示作为提高标签效率的任务指导，基于预训练的语言模型的及时学习已在名称实体识别（NER）中取得了成功。但是，以前用于几次射击的及时及时的方法具有较高的计算复杂性，零发能力差，需要手动及时工程或缺乏迅速的鲁棒性。在这项工作中，我们通过提出一种带有问答（QA）的新的基于及时的学习NER方法来解决这些缺点，称为Qaner。我们的方法包括1）将NER问题转换为质量检查公式的精致策略； 2）QA模型的迅速生成； 3）在一些带注释的NER示例上使用QA模型进行及时的调整； 4）通过提示质量检查模型来零射击。将所提出的方法与以前的方法进行比较，Qaner在推断时更快，对及时质量不敏感，并且对超参数稳健，并且表现出明显更好的低资源性能和零拍的能力。
Recently, prompt-based learning for pre-trained language models has succeeded in few-shot Named Entity Recognition (NER) by exploiting prompts as task guidance to increase label efficiency. However, previous prompt-based methods for few-shot NER have limitations such as a higher computational complexity, poor zero-shot ability, requiring manual prompt engineering, or lack of prompt robustness. In this work, we address these shortcomings by proposing a new prompt-based learning NER method with Question Answering (QA), called QaNER. Our approach includes 1) a refined strategy for converting NER problems into the QA formulation; 2) NER prompt generation for QA models; 3) prompt-based tuning with QA models on a few annotated NER examples; 4) zero-shot NER by prompting the QA model. Comparing the proposed approach with previous methods, QaNER is faster at inference, insensitive to the prompt quality, and robust to hyper-parameters, as well as demonstrating significantly better low-resource performance and zero-shot capability.
