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Simile detection is a valuable task for many natural language processing(NLP)-based applications, particularly in the field of literature. However,existing research on simile detection often relies on corpora that are limitedin size and do not adequately represent the full range of simile forms. Toaddress this issue, we propose a simile data augmentation method based on\textbf{W}ord replacement And Sentence completion using the GPT-2 languagemodel. Our iterative process called I-WAS, is designed to improve the qualityof the augmented sentences. To better evaluate the performance of our method inreal-world applications, we have compiled a corpus containing a more diverseset of simile forms for experimentation. Our experimental results demonstratethe effectiveness of our proposed data augmentation method for similedetection.

I-WAS: a Data Augmentation Method with GPT-2 for Simile Detection