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Research on data generation and augmentation has been focused majorly onenhancing generation models, leaving a notable gap in the exploration andrefinement of methods for evaluating synthetic data. There are several textsimilarity metrics within the context of generated data filtering which canimpact the performance of specific Natural Language Understanding (NLU) tasks,specifically focusing on intent and sentiment classification. In this study, wepropose RankAug, a text-ranking approach that detects and filters out the topaugmented texts in terms of being most similar in meaning with lexical andsyntactical diversity. Through experiments conducted on multiple datasets, wedemonstrate that the judicious selection of filtering techniques can yield asubstantial improvement of up to 35% in classification accuracy forunder-represented classes.

RankAug: Augmented data ranking for text classification