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

• URL: http://arxiv.org/abs/2109.07726v1
• Date: Thu, 16 Sep 2021 05:25:13 GMT
• Title: MOVER: Mask, Over-generate and Rank for Hyperbole Generation
• Authors: Yunxiang Zhang, Xiaojun Wan
• Abstract summary: We introduce a new task of hyperbole generation to transfer a literal sentence into its hyperbolic paraphrase. 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.
• Score: 82.63394952538292
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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