Advancing NLP Models with Strategic Text Augmentation: A Comprehensive Study of Augmentation Methods and Curriculum Strategies
It addresses the problem of optimizing text augmentation for NLP practitioners, but it is incremental as it builds on existing augmentation and curriculum learning methods.
This study tackled the lack of reliable evidence for text augmentation techniques in NLP by evaluating their effectiveness across tasks like topic classification and sentiment analysis, finding that specific methods combined with a novel curriculum learning approach significantly outperformed traditional training.
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.