Data augmentation techniques have been used to alleviate the problem of scarce labeled data in various NER tasks (flat, nested, and discontinuous NER tasks). Existing augmentation techniques either manipulate the words in the original text that break the semantic coherence of the text, or exploit generative models that ignore preserving entities in the original text, which impedes the use of augmentation techniques on nested and discontinuous NER tasks. In this work, we propose a novel Entity-to-Text based data augmentation technique named EnTDA to add, delete, replace or swap entities in the entity list of the original texts, and adopt these augmented entity lists to generate semantically coherent and entity preserving texts for various NER tasks. Furthermore, we introduce a diversity beam search to increase the diversity during the text generation process. Experiments on thirteen NER datasets across three tasks (flat, nested, and discontinuous NER tasks) and two settings (full data and low resource settings) show that EnTDA could bring more performance improvements compared to the baseline augmentation techniques.

翻译：数据增强技术已被用于缓解各类命名实体识别（NER）任务（包括平面、嵌套和非连续NER任务）中标注数据稀缺的问题。现有增强技术要么通过修改原始文本中的词语导致语义连贯性破坏，要么利用生成模型却忽略了对原始文本中实体的保留，这阻碍了增强技术在嵌套和非连续NER任务中的应用。本研究提出一种新颖的"实体到文本"数据增强方法——EnTDA，通过对原始文本实体列表中的实体进行增删改换操作，并利用这些增强后的实体列表为各类NER任务生成语义连贯且保留实体的文本。此外，我们引入多样化波束搜索以提高文本生成过程的多样性。在涵盖三类任务（平面、嵌套和非连续NER任务）及两种设置（全数据与低资源场景）的十三个NER数据集上的实验表明，相比基线增强技术，EnTDA能带来更大的性能提升。