MOVER: Mask, Over-generate and Rank for Hyperbole Generation
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by
Yunxiang Zhang, Xiaojun Wan
2021
Abstract
Despite being a common figure of speech, hyperbole is under-researched with
only a few studies addressing its identification task. In this paper, we
introduce a new task of hyperbole generation to transfer a literal sentence
into its hyperbolic paraphrase. To tackle the lack of available hyperbolic
sentences, we construct HYPO-XL, the first large-scale hyperbole corpus
containing 17,862 hyperbolic sentences in a non-trivial way. Based on our
corpus, we propose an unsupervised method for hyperbole generation with no need
for 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. Human evaluation results show that our
model is capable of generating hyperbolic paraphrase sentences and outperforms
several baseline systems.
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