H ypo G en: Hyperbole Generation with Commonsense and Counterfactual Knowledge (2021)

Abstract: A hyperbole is an intentional and creative exaggeration not to be taken literally. Despite its ubiquity in daily life, the computational explorations of hyperboles are scarce. In this paper, we tackle the under-explored and challenging task: sentence-level hyperbole generation. We start with a representative syntactic pattern for intensification and systematically study the semantic (commonsense and counterfactual) relationships between each component in such hyperboles. We then leverage commonsense and counterfactual inference to generate hyperbole candidates based on our findings from the pattern, and train neural classifiers to rank and select high-quality hyperboles. Automatic and human evaluations show that our generation method is able to generate hyperboles with high success rate, intensity, funniness, and creativity.

## introduction hyperboles invoke the use of exaggeration as a rhetorical device or figure of speech. it is interactive, amusing, and is the second most common among all tropes of figurative language, only after metaphors @xcite . by definition, a hyperbolic expression exceeds the credible limits of fact in the given context, whereas a literal expression agrees with the extralinguistic facts in the given context @xcite . for example in figure 1 , "the party is so lit even the wardrobe is dancing!" is considered as a hyperbole because making a lifeless object to dance is impossible; it is an intentional and creative way of exaggerating how lit the party is, and is not meant to be taken literally. in contrast, "the party is so lit (that) even my introvert friend has a good time!" is considered literal, because letting introvert people have a good time is realistic and hence not an overstatement. despite its abundance, identifying and generating hyperboles remain under-explored. com-the party is so lit (that) even the wardrobe is dancing! not capable of related to causes desire to in this example, that 'the party is lit' causes the desire to 'dance'. in addition, 'the wardrobe' is related to 'the party', and is not capable of 'dancing'. in this work, we target at generating the more challenging type of hyperboles, i.e. clause or sentence level hyperboles. according to @xcite , clause-level hyperboles consist of counterfactuality and syntactic support. inspired by the linguistic theory that 'so + adj/adv + (that) + a declarative clause' is a significant pattern with both prototypical syntactic and semantic function as overstatement @xcite , we leverage the so...that pattern, where 'that' is omittable, as a starting point to analyze and gener-ate hyperboles. @xcite state that within the so...that pattern, the sentence serves as a result of the prompt (a) and that the sentence itself creates impossible worlds. inspired by this, we systematically investigate the semantic (commonsense or counterfactual) relationships between the components within the so...that pattern. specifically, we partition each sentence into three parts: the literal prompt (a), the subject of the clause (b), and the predicate of the clause (c), as illustrated in figure 1 , and conduct detailed annotation and analysis. we discover that 6 semantic relations among a, b, and c account for over 95% of all hyperboles with the so...that pattern. this indicates that if a generation model can cover these 6 relationships, it is able to generate almost all hyperboles with such pattern. with the annotated relationships as background knowledge, we build a hyperbole generation model that takes a literal prompt (a) as input and outputs a hyperbole clause @xcite . to this end, we train a reverse comet model to generate commonsense and counterfactual phrases along with the comet model @xcite , and rank the generated candidates with a hyperbole identifier. finally, we break the restrictions of the so...that pattern, and generate hyperboles with diverse syntactic structures using a syntactically controlled paraphrase model. to the best of our knowledge, we are the first to analyze the relations of the logical components within hyperboles, and the first to automatically generate hyperboles. we summarize our contributions as follow: ## task definition given an input prompt (a), we aim to generate clause or sentence level hyperboles by completing that clause. for example, if the input is 'the party is lit', our task is to generate 'the wardrobe' (a subject b) and 'is dancing' (a predicate c) to make the full sentence ('the party is so lit that even the wardrobe is dancing') a hyperbole. ## data collection and analysis section 3.1 introduces how we collect hyperboles and non-hyperboles sentences from reddit. in section 3.2, we describe the procedure for a detailed second-round annotation: sensical relationship annotation for hyperboles with the so...that pattern. ## methodology in this section, we introduce several components for our generation model. in section 4.1, we introduce the comet model @xcite and its reverse model that favors less frequent and more creative outputs. in section 4.2 we design an algorithm to generate multiple hyperbole candidates. section 4.3 explores two possible classifiers as hyperbole identifiers to select the best candidates. a diagram is shown in figure 2 . furthermore, we propose to use paraphrasing techniques to break the pattern restriction and generate hyperboles with diverse syntactic structures in section 4.4. ## role of each component here we analyze the role of a, b, and c in hy-pogen. ablations of our own models are colored in the grey background in ## conclusion and future work we are the first to tackle the novel task of hyperbole generation at the clause or sentence level . we start with the representative so...that pattern, partition it into three components and analyze the logical relationships among them. our proposed model hypogen first generates commonsense and counterfactual predictions, and then selects top-ranking candidates as hyperboles. our experimental results show that hypogen spec is able to generate hyperboles with high success rate and high semantic intensity, funniness, and creativity scores. in addition, we propose hypopara as a diversity-oriented generation approach. followup works on hyperbole generation without relying on any patterns can use hypopara as a baseline. both our hypogen and hypopara can be applied to downstream applications such as dialog systems and storytelling, to improve their interestingness and engagement. our proposed methods are based on the pretrained language model. it is known that pretrained language models could capture the bias reflected in the training data @xcite . considering the nature of exaggeration or overstatement, the context and sentiment of the literal input prompt also affect the our generated hyperboles. therefore, our models may potentially generate offensive content for certain groups or individuals. we suggest to carefully examine the potential biases before deploying the models to real-world applications.