A Survey of Data Augmentation Methods in Natural Language Processing

[Objective] This paper comprehensively reviews the methods of text augmentation to reveal their current state of development and trends. [Coverage] Using“textual data augmentation”and“text augmentation” as search terms to retrieve literature from Web of Science, Google Scholar and CNKI, we screened out a total of 88 representative papers for review. [Methods] Text augmentation methods were categorized and summarized according to the objects of operation, the details of implementation and the diversity of generated results. On this basis, we conducted a thorough comparison of various methods with regards to their granularity, strengths, weaknesses and applications. [Results] Text augmentation approaches can be divided into text space-based methods and vector space-based methods. The former is intuitive and easily interpretable but may compromise the overall semantic structure of the text, while the latter can directly manipulate semantic features but incurs higher computational complexity. Current studies frequently necessitate external knowledge resources, such as heuristic guidelines and task-specific data. Moreover, the introduction of deep learning algorithms can enhance the novelty and diversity of generated data. [Limitations] We primarily offer a systematic examination of technical principles and performance characteristics of advanced methods, without assessing the developmental stage of platform tools quantitatively. Besides, the analysis is grounded in our chosen literatures and may not encompass all potential application scenarios of text augmentation methods. [Conclusions] Future work should pay more attention to enriching and refining the evaluation metrics for text augmentation techniques and increasing their robustness across different downstream tasks by prompt learning. Retrieval-augmented generation and graph neural networks should be taken seriously for addressing the challenges posed by lengthy texts and limited resources, which can further unlock the potential of text augmentation methods in the field of natural language processing. © 2025 Chinese Academy of Sciences. All rights reserved.