  Research on data generation and augmentation has been focused majorly on
enhancing generation models, leaving a notable gap in the exploration and
refinement of methods for evaluating synthetic data. There are several text
similarity metrics within the context of generated data filtering which can
impact the performance of specific Natural Language Understanding (NLU) tasks,
specifically focusing on intent and sentiment classification. In this study, we
propose RankAug, a text-ranking approach that detects and filters out the top
augmented texts in terms of being most similar in meaning with lexical and
syntactical diversity. Through experiments conducted on multiple datasets, we
demonstrate that the judicious selection of filtering techniques can yield a
substantial improvement of up to 35% in classification accuracy for
under-represented classes.
