# A Match Made in Heaven: A Multi-task Framework for Hyperbole and Metaphor Detection

arXiv: https://arxiv.org/abs/2305.17480 · HTML: https://mlsanity.com/paper/2305.17480/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 27 May 2023.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories](https://mlsanity.com/paper/2104.13615/) (2021)
- [Metaphors in Pre-Trained Language Models: Probing and Generalization Across Datasets and Languages](https://mlsanity.com/paper/2203.14139/) (2022)
- [HypoGen: Hyperbole Generation with Commonsense and Counterfactual Knowledge](https://mlsanity.com/paper/2109.05097/) (2021)
- [MOVER: Mask, Over-generate and Rank for Hyperbole Generation](https://mlsanity.com/paper/2109.07726/) (2021)

## Built on by (most cited first, 10 of 10)

- [Unveiling the Invisible: Captioning Videos with Metaphors](https://mlsanity.com/paper/2406.04886/) (2024)
- [Meta4XNLI: A Crosslingual Parallel Corpus for Metaphor Detection and Interpretation](https://mlsanity.com/paper/2404.07053/) (2024)
- [Enhancing Hyperbole and Metaphor Detection with Their Bidirectional Dynamic Interaction and Emotion Knowledge](https://mlsanity.com/paper/2506.15504/) (2025)
- [Toxicity Begets Toxicity: Unraveling Conversational Chains in Political Podcasts](https://mlsanity.com/paper/2501.12640/) (2025)
- [A Multidimensional Framework for Evaluating Lexical Semantic Change with Social Science Applications](https://mlsanity.com/paper/2406.06052/) (2024)
- [Deep Learning-Based Knowledge Injection for Metaphor Detection: A Comprehensive Review](https://mlsanity.com/paper/2308.04306/) (2023)
- [Figuratively Speaking: Authorship Attribution via Multi-Task Figurative Language Modeling](https://mlsanity.com/paper/2406.08218/) (2024)
- [Stereotype Detection as a Catalyst for Enhanced Bias Detection: A Multi-Task Learning Approach](https://mlsanity.com/paper/2507.01715/) (2025)
- [Towards High-Level Semantic Intelligence](https://mlsanity.com/paper/2607.24082/) (2026)
- [Wisdom in Unity: The Role of Multilingual Training in Figurative Language Identification in Proverbs](https://mlsanity.com/paper/2608.08090/) (2026)

## Abstract (arXiv)

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.