mlsanityData to 14 Oct 2026

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

arXiv 2109.07726 ↗First posted 16 Sep 2021cs.CLcited by 13

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Abstract (arXiv)

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.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 32 of its 57 references that are in the map. First posted on arXiv 16 Sep 2021.

Builds on

32 of 57 references.

References · 32

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20 more
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Citing papers per year: 2022: 1; 2023: 6; 2024: 3; 2025: 1; 2026: 2.

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