# MOVER: Mask, Over-generate and Rank for Hyperbole Generation

arXiv: https://arxiv.org/abs/2109.07726 · HTML: https://mlsanity.com/paper/2109.07726/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 57 references that are in the map.
- First posted on arXiv 16 Sep 2021.

## Builds on (most cited first, 30 of 32)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [PAWS: Paraphrase Adversaries from Word Scrambling](https://mlsanity.com/paper/1904.01130/) (2019)
- [Toward Controlled Generation of Text](https://mlsanity.com/paper/1703.00955/) (2017)
- [Why We Need New Evaluation Metrics for NLG](https://mlsanity.com/paper/1707.06875/) (2017)
- [Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer](https://mlsanity.com/paper/1804.06437/) (2018)
- [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://mlsanity.com/paper/2010.05700/) (2020)
- [Deep Learning for Text Style Transfer: A Survey](https://mlsanity.com/paper/2011.00416/) (2020)
- [A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer](https://mlsanity.com/paper/1905.10060/) (2019)
- [Long and Diverse Text Generation with Planning-based Hierarchical Variational Model](https://mlsanity.com/paper/1908.06605/) (2019)
- [MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding](https://mlsanity.com/paper/2103.06779/) (2021)
- [Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs](https://mlsanity.com/paper/2101.10579/) (2021)
- [Pun Generation with Surprise](https://mlsanity.com/paper/1904.06828/) (2019)
- [R3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge](https://mlsanity.com/paper/2004.13248/) (2020)
- [Metaphor Generation with Conceptual Mappings](https://mlsanity.com/paper/2106.01228/) (2021)
- [Learning Paraphrastic Sentence Embeddings from Back-Translated Bitext](https://mlsanity.com/paper/1706.01847/) (2017)
- [HypoGen: Hyperbole Generation with Commonsense and Counterfactual Knowledge](https://mlsanity.com/paper/2109.05097/) (2021)
- [Multiple-Attribute Text Style Transfer](https://mlsanity.com/paper/1811.00552/) (2018)
- [Unsupervised Paraphrasing without Translation](https://mlsanity.com/paper/1905.12752/) (2019)
- [Syntax-Infused Variational Autoencoder for Text Generation](https://mlsanity.com/paper/1906.02181/) (2019)
- [Pun-GAN: Generative Adversarial Network for Pun Generation](https://mlsanity.com/paper/1910.10950/) (2019)
- [A Corpus of Sentence-level Revisions in Academic Writing: A Step towards Understanding Statement Strength in Communication](https://mlsanity.com/paper/1405.1439/) (2014)
- [Annotation and Classification of Sentence-level Revision Improvement](https://mlsanity.com/paper/1909.05309/) (2019)
- [Writing Polishment with Simile: Task, Dataset and A Neural Approach](https://mlsanity.com/paper/2012.08117/) (2020)
- [Metaphoric Paraphrase Generation](https://mlsanity.com/paper/2002.12854/) (2020)
- [Style Example-Guided Text Generation using Generative Adversarial Transformers](https://mlsanity.com/paper/2003.00674/) (2020)

## Built on by (most cited first, 13 of 13)

- [Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark](https://mlsanity.com/paper/2305.14938/) (2023)
- [The Parrot Dilemma: Human-Labeled vs. LLM-augmented Data in Classification Tasks](https://mlsanity.com/paper/2304.13861/) (2023)
- [SoMeLVLM: A Large Vision Language Model for Social Media Processing](https://mlsanity.com/paper/2402.13022/) (2024)
- [A Match Made in Heaven: A Multi-task Framework for Hyperbole and Metaphor Detection](https://mlsanity.com/paper/2305.17480/) (2023)
- [SOCIALITE-LLAMA: An Instruction-Tuned Model for Social Scientific Tasks](https://mlsanity.com/paper/2402.01980/) (2024)
- [Creative Data Generation: A Review Focusing on Text and Poetry](https://mlsanity.com/paper/2305.08493/) (2023)
- [HAUSER: Towards Holistic and Automatic Evaluation of Simile Generation](https://mlsanity.com/paper/2306.07554/) (2023)
- [Enhancing Hyperbole and Metaphor Detection with Their Bidirectional Dynamic Interaction and Emotion Knowledge](https://mlsanity.com/paper/2506.15504/) (2025)
- [Multi-Figurative Language Generation](https://mlsanity.com/paper/2209.01835/) (2022)
- [Towards Zero-Shot Frame Semantic Parsing with Task Agnostic Ontologies and Simple Labels](https://mlsanity.com/paper/2305.03793/) (2023)
- [Computational Approaches to the Detection of Lesser-Known Rhetorical Figures: A Systematic Survey and Research Challenges](https://mlsanity.com/paper/2406.16674/) (2024)
- [Are Emotion and Rhetoric Neurons in LLM? Neuron Recognition and Adaptive Masking for Emotion-Rhetoric Prediction Steering](https://mlsanity.com/paper/2604.17255/) (2026)
- [Towards High-Level Semantic Intelligence](https://mlsanity.com/paper/2607.24082/) (2026)

## Abstract (arXiv)

Despite being a common figure of speech, hyperbole is under-researched in Figurative Language Processing. In this paper, we tackle the challenging task of hyperbole generation to transfer a literal sentence into its hyperbolic paraphrase. To address the lack of available hyperbolic sentences, we construct HYPO-XL, the first large-scale English hyperbole corpus containing 17,862 hyperbolic sentences in a non-trivial way. Based on our corpus, we propose an unsupervised method for hyperbole generation that does not require parallel literal-hyperbole pairs. During training, we fine-tune BART to infill masked hyperbolic spans of sentences from HYPO-XL. During inference, we mask part of an input literal sentence and over-generate multiple possible hyperbolic versions. Then a BERT-based ranker selects the best candidate by hyperbolicity and paraphrase quality. Automatic and human evaluation results show that our model is effective at generating hyperbolic paraphrase sentences and outperforms several baseline systems.