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.

本研究提出了一种名为 RankAug 的文本排名方法，通过多样性的词汇和句法，检测和过滤出最具相似意义的顶级增强文本，从而改善生成数据过滤在自然语言理解任务中的性能，特别是意图和情感分类。通过对多个数据集进行实验，我们证明了过滤技术的精心选择可以显著提高少数派分类的准确性，提高了多达 35%。