3Rs: Data Augmentation Techniques Using Document Contexts For Low-Resource Chinese Named Entity Recognition

With recent advances of neural networks and pre-training techniques, Chinese Named Entity Recognition (NER) has achieved great progress in recent years. However, NER systems still have the problem of generalization ability issues due to lack of annotated data, and current NER models mostly consider input sentences individually, which prevent models from further exploiting cross-sentence document context in training. With regard of these problems, this paper present new insights into Chinese NER and propose 3Rs: three data augmentation methods incorporating document-level information for NER through random concatenating, random swapping and random erasing, which are inspired by some multi-sample data augmentation techniques in computer vision fields, aiming to reorganize the composition of training sentences, and generate more training examples with less human efforts. We conduct extensive experiments on two Chinese datasets, and introduce a two-level attacking method to audit robustness performance. Our experiment results show that even the best model can obtain a better accuracy and robustness, especially for smaller training sets, therefore alleviating performance bottlenecks on low-resource conditions.