Text Augmentation

Text Augmentation is a technique in natural language processing aimed at transforming and expanding existing text data to generate new training samples, thereby enhancing the model's generalization and robustness. This method increases data diversity, helping the model learn language features more effectively, reduce the risk of overfitting, and improve its performance in various scenarios.