Text data augmentation and pre-trained Language Model for enhancing text classification of low-resource languages.

Background: In the domain of natural language processing (NLP), the development and success of advanced language models are predominantly anchored in the richness of available linguistic resources. Languages such as Azerbaijani, which is classified as a low-resource, often face challenges arising from limited labeled datasets, consequently hindering effective model training.

Methodology: The primary objective of this study was to enhance the effectiveness and generalization capabilities of news text classification models using text augmentation techniques.