Recently, data augmentation (DA) methods have been proven to be effective for
pre-trained language models (PLMs) in low-resource settings, including few-shot
named entity recognition (NER). However, conventional NER DA methods are mostly
aimed at sequence labeling models, i.e., token-level classification, and few
are compatible with unified autoregressive generation frameworks, which can
handle a wider range of NER tasks, such as nested NER. Furthermore, these
generation frameworks have a strong assumption that the entities will appear in
the target sequence with the same left-to-right order as the source sequence.
In this paper, we claim that there is no need to keep this strict order, and
more diversified but reasonable target entity sequences can be provided during
the training stage as a novel DA method. Nevertheless, a naive mixture of
augmented data can confuse the model since one source sequence will then be
paired with different target sequences. Therefore, we propose a simple but
effective Prompt Ordering based Data Augmentation (PODA) method to improve the
training of unified autoregressive generation frameworks under few-shot NER
scenarios. Experimental results on three public NER datasets and further
analyses demonstrate the effectiveness of our approach.

本文提出了一种名为基于提示排序的数据增强方法（PODA），使用于预训练语言模型在低资源环境下执行少样本命名实体识别任务，PODA 方法通过提供不按来源序列严格排序但合理多样化的目标实体序列来训练模型，可以加强在各种 NER 任务中的效果表现。