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Data augmentation approaches in natural language processing: A survey

AI open, 2022, Vol.3, p.71-90 [Peer Reviewed Journal]

2022 The Authors ;ISSN: 2666-6510 ;EISSN: 2666-6510 ;DOI: 10.1016/j.aiopen.2022.03.001

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  • Title:
    Data augmentation approaches in natural language processing: A survey
  • Author: Li, Bohan ; Hou, Yutai ; Che, Wanxiang
  • Subjects: Data augmentation ; Machine learning ; Natural language processing
  • Is Part Of: AI open, 2022, Vol.3, p.71-90
  • Description: As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where deep learning techniques may fail. It is widely applied in computer vision then introduced to natural language processing and achieves improvements in many tasks. One of the main focuses of the DA methods is to improve the diversity of training data, thereby helping the model to better generalize to unseen testing data. In this survey, we frame DA methods into three categories based on the diversity of augmented data, including paraphrasing, noising, and sampling. Our paper sets out to analyze DA methods in detail according to the above categories. Further, we also introduce their applications in NLP tasks as well as the challenges. Some useful resources are provided in Appendix A.
  • Publisher: Elsevier B.V
  • Language: English
  • Identifier: ISSN: 2666-6510
    EISSN: 2666-6510
    DOI: 10.1016/j.aiopen.2022.03.001
  • Source: Open Access: DOAJ Directory of Open Access Journals
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