A Match Made in Heaven: A Multi-task Framework for Hyperbole and Metaphor Detection (2023)

Abstract: Hyperbole and metaphor are common in day-to-day communication (e.g., “I am in deep trouble”: how does trouble have depth?), which makes their detection important, especially in a conversational AI setting. Existing approaches to automatically detect metaphor and hyperbole have studied these language phenomena independently, but their relationship has hardly, if ever, been explored computationally. In this paper, we propose a multi-task deep learning framework to detect hyperbole and metaphor simultaneously. We hypothesize that metaphors help in hyperbole detection, and vice-versa. To test this hypothesis, we annotate two hyperbole datasets- HYPO and HYPO-L- with metaphor labels. Simultaneously, we annotate two metaphor datasets- TroFi and LCC- with hyperbole labels. Experiments using these datasets give an improvement of the state of the art of hyperbole detection by 12%. Additionally, our multi-task learning (MTL) approach shows an improvement of up to 17% over single-task learning (STL) for both hyperbole and metaphor detection, supporting our hypothesis. To the best of our knowledge, ours is the first demonstration of computational leveraging of linguistic intimacy between metaphor and hyperbole, leading to showing the superiority of MTL over STL for hyperbole and metaphor detection.

## introduction the use of figurative language is very common in natural discourse, and it is reflected in the content generated in social media networks @xcite . figurative languages are used to establish some communicative goals such as expressing a negative emotion, drawing attention to a part of the text, or adding interest to a subject. @xcite . the understanding of figurative languages like sarcasm, metaphor, simile, irony, and hyperbole is crucial for many nlp tasks such as building accurate sentiment analysis systems or developing conversational ai systems that can hold meaningful conversations (figure 1 ). this has led to great interest and value in understanding these figurative languages. figurative languages like metaphor @xcite and sarcasm @xcite are studied extensively while hyperbole remains less explored. metaphor is the most common choice of figurative language, while hyperbole is the second most adopted rhetorical device in communication @xcite and hence it is important to study and process them automatically. hyperbole is a figurative language that uses exaggeration to emphasize a point, while metaphor makes a comparison between two things to indicate a resemblance. ## background and definitions metaphor metaphor is a literary device that uses an implicit comparison to drive home a new meaning. metaphors consist of a source and target domain in which the features from the source domain are related to the features in the target domain through comparable properties @xcite . for instance, "life is a journey," implies a comparison between life and journey through the idea of having a beginning and an end. in this work, we do not consider similes as metaphors as they make an explicit comparison. hyperbole hyperbole is a figurative language in which the literal meaning is exaggerated intentionally. it exaggerates expressions and blows them up beyond the point they are perceived naturally with the objective of emphasizing them @xcite . for example, "i'm tired, i can't lift my hand," exaggerates the speaker's exhaustion. figure 3 shows examples of metaphor and hyperbole. ## related work metaphors and hyperboles are the most used figures of speech in everyday utterances @xcite . in recent years, significant efforts have been made to understand metaphors and hyperboles, giving rise to interesting techniques to automatically detect and generate them. @xcite introduced hyperbole detection as a binary classification task, using traditional machine learning algorithms. they also released a dataset named 'hypo' for hyperbole detection. @xcite introduced 'hypo-cn', a chinese dataset for hyperbole detection, and showed that deep learning models can perform better at hyperbole detection with increased data. @xcite used a bert @xcite based detection system that used the literal sentences of the hyperbolic counterparts to identify the hyperbolic and nonhyperbolic use of words and phrases. they also released a test suite for evaluating models. @xcite proposed a hyperbole generation task. zhang and wan (2022) introduced an unsupervised approach for generating hyperbolic sentences from literal sentences and introduced two new datasets 'hypo-xl' and 'hypo-l' for their experiments. metaphors have been extensively studied even before hyperbole detection was introduced. @xcite introduced the tsv dataset with 884 metaphorical and non-metaphorical adjective-noun (an) phrases. they showed that conceptual mapping learnt between literal and metaphorical words is transferable across languages. @xcite introduced the lcc dataset which contains sentence-level annotations for metaphors in four languages totaling 188, 741 instances. @xcite studied metaphor at the word level and was the first to include function words for metaphor detection with the new vua dataset. @xcite introduced the trofi dataset that consists of verbs in their literal and metaphoric form. in recent years, metaphor detection has been explored with the aid of large language models. @xcite used the contextual embeddings from bert @xcite and roberta @xcite to classify metaphorical sentences. @xcite probed and analyzed the metaphorical knowledge gained by large language models by testing them on metaphor datasets across languages. previous research on metaphor and hyperbole detection typically treats these figurative language forms separately, despite their common properties. in this work, we propose a multi-task approach that simultaneously detects both hyperboles and metaphors, and demonstrate that this approach outperforms individual detection tasks with experimental results and detailed analysis. ## task formulation for a sentence x and a corresponding label y or labels y 1 , .., y k (@xmath0, we can mathematically formulate the different learning tasks shown in figure 2 as: single task learning (stl) here, the loss is a binary cross-entropy loss: ## datasets and annotations in this section, we delve into the hyperbole and metaphor datasets used and their annotation details. ## experiments we conduct four experiments: 1) comparing stl and mtl-f on hyperbole and metaphor datasets, 2) comparing stl, mtl-e, and mtl-f models, 3) obtaining sentence-level benchmark results on the metaphor dataset, and 4) comparing with established baselines for the hyperbole dataset. for our experiments, we used label-balanced metaphor datasets to address the imbalance caused by fewer hyperbole (refer to appendix a.2). to ensure a fair comparison, we used mean 10-fold cross-validation obtained over three different runs to compare our models. however, we did not compare our results with existing work on metaphor detection as it does token-level instead of sentencelevel metaphor prediction. finally, we used simple models to highlight the efficacy of a multi-tasked framework for a sophisticated task. ## conclusion and future work we have presented a novel multi-tasking approach to the detection of hyperboles and metaphors. we augmented the annotations of two hyperbole datasets with metaphor labels and that of two metaphor datasets with hyperbole labels. this allowed multi-task learning of metaphor and hyperbole detection, which outperforms single-task learning on both tasks. we establish a new sota for hyperbole detection and a new benchmark for sentence-level metaphor detection. the take-away message is that metaphor and hyperbole detection help each other and should be done together. we plan to extend our framework of exploiting linguistic relatedness and thereby creating mtl detection systems, to all forms of figurative languages like proverbs, idioms, humour, similes, and so on.