MOVER : Mask, Over-generate and Rank for Hyperbole Generation (2022)

Abstract: 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.

## introduction hyperbole is a figure of speech that deliberately exaggerates a claim or statement to show emphasis or express emotions. if a referent has a feature x, a hyperbole exceeds the credible limits of fact in the given context and presents it as having more of that x than warranted by reality @xcite ). take the following example, "i won't wait for you: it took you centuries to get dressed." it over-blows the time for someone to get dressed with a single word "centuries" and thus creates a heightened effect. from a syntactic point of view, @xcite classifies hyperbole into word-level, phraselevel and clause-level types, and conclude that the former two types are more common in english. although hyperbole is considered as the second most frequent figurative device @xcite , it has received less empirical attention in the nlp community. recently @xcite addressed the generation of clause-level hyperbole. in this paper, we instead focus on word-level and phrase-level hyperbole, which can be unified as span-level hyperbole. to tackle the hyperbole generation problem we need to address three main challenges: in order to address the above challenges, we propose mover (mask, over-generate and rank), an unsupervised approach to generating hyperbolic paraphrase from literal input. our approach does not require parallel data for training, thus alleviating the issue of scarce data. still, we need a non-parallel corpus containing as much hyperbolic sentences as possible. to this end, we first build a large-scale english hyperbole corpus hypo-xl in a weakly supervised way. based on the intuition that the hyperbolic effect of a sentence is realized by a single word or phrase within it, we introduce a sub-task of hyperbolic span extraction. we identify several possible ngrams of a hyperbolic sentence that can cause the hyperbolic bent with syntactic and semantic features. we apply this masking approach to sentences in hypo-xl and teach a pretrained seq2seq transformer, bart @xcite , to infill the words in missing hyperbolic spans. this increases the probability of generating hyperbolic texts instead of literal ones. during inference, given a single literal sentence, our system provides multiple masked versions for inputs to bart and gen-erates potential hyperbolic sentences accordingly. to select the best one for output, we leverage a bert-based ranker to achieve a satisfying tradeoff between hyperbolicity and paraphrase quality. our contributions are three-fold: ## hypo-xl: hyperbole corpus collection the availability of large-scale corpora can facilitate the development of figurative language generation with pretrained models, as is shown by chakrabarty et al. (2020b) on simile generation and chakrabarty et al. (2021) on metaphor generation. however, datasets for hyperbole are scarce. troiano et al. (2018) built an english corpus hypo containing 709 triplets [hypo, para, non_hypo], where hypo refers to a hyperbolic sentence, para denotes the literal paraphrase of hypo and non_hypo means a non-hyperbolic sentence that contains the same hyperbolic word or phrase as hypo but with a literal connotation. the size of this dataset is too small to train a deep learning model for hyperbole detection and generation. to tackle the lack of hyperbole data, we propose to enlarge the hyperbolic sentences of hypo in a weakly supervised way and build a large-scale english corpus of 17,862 hyperbolic sentences, namely hypo-xl. we would like to point out that this is a non-parallel corpus containing only hyperbolic sentences without their paraphrase counterparts, because our hyperbole generation approach (section 3) does not require parallel training data. the creation of hypo-xl consists of two steps: 1. we first train a bert-based binary classifier on hypo and retrieve possible hyperbolic sentences from an online corpus. ## hyperbole generation we propose an unsupervised approach to generate hyperbolic paraphrase from a literal sentence with bart @xcite ) such that we do not require parallel literal-hyperbole pairs. 7 an overview of our hyperbole generation pipeline is shown in figure 1 . it consists of two steps during training: ## experiments there are no existing models applied to the task of word-or phrase-level hyperbole generation. to compare the quality of the generated hyperboles, we benchmark our mover system against three baseline systems adapted from related tasks. ## conclusion we tackle the challenging task of figurative language generation: hyperbole generation from literal sentences. we build the first large-scale hyperbole corpus hypo-xl and propose an unsupervised approach mover for generating hyperbole in a controllable way. the results of automatic and human evaluation show that our model is successful in generating hyperbole. the proposed generation pipeline has better interpretability and flexibility compared to the potential end-to-end methods. in future, we plan to apply our "mask-overgeneraterank" approach to the generation of other figurative languages, such as metaphor and irony.