  This study conducts a thorough evaluation of text augmentation techniques
across a variety of datasets and natural language processing (NLP) tasks to
address the lack of reliable, generalized evidence for these methods. It
examines the effectiveness of these techniques in augmenting training sets to
improve performance in tasks such as topic classification, sentiment analysis,
and offensive language detection. The research emphasizes not only the
augmentation methods, but also the strategic order in which real and augmented
instances are introduced during training. A major contribution is the
development and evaluation of Modified Cyclical Curriculum Learning (MCCL) for
augmented datasets, which represents a novel approach in the field. Results
show that specific augmentation methods, especially when integrated with MCCL,
significantly outperform traditional training approaches in NLP model
performance. These results underscore the need for careful selection of
augmentation techniques and sequencing strategies to optimize the balance
between speed and quality improvement in various NLP tasks. The study concludes
that the use of augmentation methods, especially in conjunction with MCCL,
leads to improved results in various classification tasks, providing a
foundation for future advances in text augmentation strategies in NLP.
