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