  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.
