Identifying Exaggerated Language (2020)

Abstract: While hyperbole is one of the most prevalent rhetorical devices, it is arguably one of the least studied devices in the figurative language processing community. We contribute to the study of hyperbole by (1) creating a corpus focusing on sentence-level hyperbole detection, (2) performing a statistical and manual analysis of our corpus, and (3) addressing the automatic hyperbole detection task.

## introduction recent years have seen a surge of interest in the automatic processing of figurative language in the nlp community, as evidenced by the successful organization of the naacl 2018 workshop on figurative language processing. much of the work on figurative language processing conducted so far, however, has focused on metaphor and metonymy @xcite , and more recently, sarcasm @xcite , idioms @xcite , and puns @xcite . in particular, hyperbole, also known as exaggeration, is a relatively under-studied phenomenon in the community. this is somewhat surprising, especially given that the prevalence of hyperbole as a rhetorical device is only second to metaphor @xcite . humans exaggerate in different situations for various purposes, such as creating amusement, expressing emotion and drawing attention @xcite . the vast amount of work on metaphor detection in the past few years was stimulated in large part by the availability of standard evaluation corpora. progress on the computational study of exaggeration, on the other hand, is hindered by the lack of annotated resources. to our knowledge, hypo, the first dataset that focuses on exaggeration, was only released in late 2018 @xcite . hypo consists of 709 hyperbolic sentences, each of which has a non-hyperbolic version created by manually paraphrasing its hyperbolic counterpart. given the dataset, troiano et al. introduced the task of automatic hyperbole detection, where the goal is to determine whether a sentence is a hyperbole. given the status quo, our goal is to further the computational study of exaggeration. specifically, our contributions in this work are three-fold. first, we create hypo-cn, the first chinese dataset on exaggeration. hypo-cn consists of 4762 sentences, of which 2680 are hyperbolic and 2082 are nonhyperbolic. to stimulate research on the computational study of exaggeration, we make hypo-cn publicly available. we believe that this dataset can complement @xcite s english dataset and facilitate a cross-lingual study of exaggeration. our second contribution involves conducting an empirical analysis of hypo-cn. we perform two kinds of analysis. first, we conduct a statistical analysis in an attempt to answer various questions involving exaggeration, such as: (1) are there strong lexical indicators of hyperbole; (2) how lexically diverse are the non-hyperbolic versions of a given hyperbolic sentence; and (3) how lexically diverse are the hyperbolic versions of a given nonhyperbolic sentence? second, we conduct a manual analysis to identify the major strategies used by humans to overstate. we believe our analysis can advance the computational study of exaggeration and allow us to shed light on a number of interesting questions involving exaggeration. finally, we perform preliminary experiments on the automatic hyperbole detection task using hypo-cn. unlike @xcite , who employed only traditional (i.e., non-neural) learners for hyperbole detection, we examine the use of deep learning for model training, with the goal of understanding whether state-of-the-art learning techniques can offer better results. we show that the best deep learner outperforms the best traditional learner by 11.0% points in accuracy. these results provide suggestive evidence that hyperbole detection is indeed a task that requires a deep understanding of text semantics, as this is what primarily distinguishes a deep learner from a traditional learner. ## dataset creation in this section, we describe the steps involved in the creation of our chinese dataset, hypo-cn. ## automatic hyperbole detection next, we present preliminary results on the automatic hyperbole detection task. we cast it as a supervised binary classification problem where we predict whether a sentence is hyperbolic or not. ## conclusion we presented an empirical study of exaggeration, which is one of the most prevalent and yet one of the least studied rhetorical devices from a computational perspective. our contributions lie in (1) creating a chinese corpus focused on exaggeration, (2) identifying different strategies used by humans for exaggeration and (3) showing that deep learners substantially outperform traditional learners on automatic hyperbole detection. the statistical and manual analyses of our corpus, which is absent from other computational studies on exaggeration, have shed light on various interesting questions about this rhetorical device. to stimulate research on this topic, we make hypo-cn publicly available. 8 in future work, we plan to use hypo and hypo-cn to conduct a cross-lingual study on whether there are differences in the way exaggeration is expressed in english and chinese.