Advancing NLP Models with Strategic Text Augmentation: A Comprehensive Study of Augmentation Methods and Curriculum Strategies

arXiv:2402.09141v1 Announce Type: new
Abstract: 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 …

abstract and natural language processing arxiv augmentation cs.ai cs.cl curriculum datasets evaluation evidence generalized language language processing natural natural language natural language processing nlp nlp models processing strategies study tasks text type