# Computational Approaches to the Detection of Lesser-Known Rhetorical Figures: A Systematic Survey and Research Challenges

arXiv: https://arxiv.org/abs/2406.16674 · HTML: https://mlsanity.com/paper/2406.16674/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 136 references that are in the map.
- First posted on arXiv 24 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
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- [HypoGen: Hyperbole Generation with Commonsense and Counterfactual Knowledge](https://mlsanity.com/paper/2109.05097/) (2021)
- [Automatic Sarcasm Detection: A Survey](https://mlsanity.com/paper/1602.03426/) (2016)
- [Potential Idiomatic Expression (PIE)-English: Corpus for Classes of Idioms](https://mlsanity.com/paper/2105.03280/) (2021)
- [CATs are Fuzzy PETs: A Corpus and Analysis of Potentially Euphemistic Terms](https://mlsanity.com/paper/2205.02728/) (2022)
- [MOVER: Mask, Over-generate and Rank for Hyperbole Generation](https://mlsanity.com/paper/2109.07726/) (2021)
- [Target Word Masking for Location Metonymy Resolution](https://mlsanity.com/paper/2010.16097/) (2020)
- [Features of Perceived Metaphoricity on the Discourse Level: Abstractness and Emotionality](https://mlsanity.com/paper/2205.08939/) (2022)
- [ConFiguRe: Exploring Discourse-level Chinese Figures of Speech](https://mlsanity.com/paper/2209.07678/) (2022)
- [Impact of Target Word and Context on End-to-End Metonymy Detection](https://mlsanity.com/paper/2112.03256/) (2021)

## Abstract (arXiv)

Rhetorical figures play a major role in our everyday communication as they make text more interesting, more memorable, or more persuasive. Therefore, it is important to computationally detect rhetorical figures to fully understand the meaning of a text. We provide a comprehensive overview of computational approaches to lesser-known rhetorical figures. We explore the linguistic and computational perspectives on rhetorical figures, emphasizing their significance for the domain of Natural Language Processing. We present different figures in detail, delving into datasets, definitions, rhetorical functions, and detection approaches. We identified challenges such as dataset scarcity, language limitations, and reliance on rule-based methods.