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Despite being a common figure of speech, hyperbole is under-researched inFigurative Language Processing. In this paper, we tackle the challenging taskof hyperbole generation to transfer a literal sentence into its hyperbolicparaphrase. To address the lack of available hyperbolic sentences, we constructHYPO-XL, the first large-scale English hyperbole corpus containing 17,862hyperbolic sentences in a non-trivial way. Based on our corpus, we propose anunsupervised method for hyperbole generation that does not require parallelliteral-hyperbole pairs. During training, we fine-tune BART to infill maskedhyperbolic spans of sentences from HYPO-XL. During inference, we mask part ofan input literal sentence and over-generate multiple possible hyperbolicversions. Then a BERT-based ranker selects the best candidate by hyperbolicityand paraphrase quality. Automatic and human evaluation results show that ourmodel is effective at generating hyperbolic paraphrase sentences andoutperforms several baseline systems.

MOVER: Mask, Over-generate and Rank for Hyperbole Generation