Text augmentation is a technique that is used in Natural Language Processing (NLP) to generate additional data by modifying or creating new text from existing text data. Text augmentation involves techniques such as character swapping, noise injection, synonym replacement, word deletion, word insertion, and word swapping. Image and text augmentation have the same goal. They strive to increase the size of the training dataset and improve AI prediction accuracy.
Text augmentation is relatively more challenging to evaluate because it is not as intuitive as image augmentation. The intent of an image augmentation technique is clear, such as flipping a photo, but a character-swapping technique will be disorienting to the reader. Therefore, readers might perceive the benefits as subjective.
The effectiveness of text augmentation depends on the quality of the generated data and the specific NLP task being performed. It can be challenging to determine the appropriate...