# R3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge

arXiv: https://arxiv.org/abs/2004.13248 · HTML: https://mlsanity.com/paper/2004.13248/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2020.

## Built on by (most cited first, 30 of 32)

- [Symbolic Knowledge Distillation: from General Language Models to Commonsense Models](https://mlsanity.com/paper/2110.07178/) (2021)
- [The Perils of Using Mechanical Turk to Evaluate Open-Ended Text Generation](https://mlsanity.com/paper/2109.06835/) (2021)
- [RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge](https://mlsanity.com/paper/2101.00376/) (2021)
- [It's not Rocket Science : Interpreting Figurative Language in Narratives](https://mlsanity.com/paper/2109.00087/) (2021)
- [When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues](https://mlsanity.com/paper/2203.06419/) (2022)
- [Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources](https://mlsanity.com/paper/2103.11320/) (2021)
- [Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations](https://mlsanity.com/paper/2106.13876/) (2021)
- [HypoGen: Hyperbole Generation with Commonsense and Counterfactual Knowledge](https://mlsanity.com/paper/2109.05097/) (2021)
- [AmbiPun: Generating Humorous Puns with Ambiguous Context](https://mlsanity.com/paper/2205.01825/) (2022)
- [Multi-source Semantic Graph-based Multimodal Sarcasm Explanation Generation](https://mlsanity.com/paper/2306.16650/) (2023)
- [MOVER: Mask, Over-generate and Rank for Hyperbole Generation](https://mlsanity.com/paper/2109.07726/) (2021)
- [GD-COMET: A Geo-Diverse Commonsense Inference Model](https://mlsanity.com/paper/2310.15383/) (2023)
- [What happens before and after: Multi-Event Commonsense in Event Coreference Resolution](https://mlsanity.com/paper/2302.09715/) (2023)
- [!Qu'e maravilla! Multimodal Sarcasm Detection in Spanish: a Dataset and a Baseline](https://mlsanity.com/paper/2105.05542/) (2021)
- [Creativity in AI: Progresses and Challenges](https://mlsanity.com/paper/2410.17218/) (2024)
- [A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectives](https://mlsanity.com/paper/2209.04712/) (2022)
- [Controllable Text Generation for Open-Domain Creativity and Fairness](https://mlsanity.com/paper/2209.12099/) (2022)
- [Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal Dialogues](https://mlsanity.com/paper/2211.11049/) (2022)
- [Sentiment-enhanced Graph-based Sarcasm Explanation in Dialogue](https://mlsanity.com/paper/2402.03658/) (2024)
- [From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey](https://mlsanity.com/paper/2110.15871/) (2021)
- [Creative Data Generation: A Review Focusing on Text and Poetry](https://mlsanity.com/paper/2305.08493/) (2023)
- ["Im not Racist but...": Discovering Bias in the Internal Knowledge of Large Language Models](https://mlsanity.com/paper/2310.08780/) (2023)
- [kogito: A Commonsense Knowledge Inference Toolkit](https://mlsanity.com/paper/2211.08451/) (2022)
- [ImpScore: A Learnable Metric For Quantifying The Implicitness Level of Sentence](https://mlsanity.com/paper/2411.05172/) (2024)
- [Target-Augmented Shared Fusion-based Multimodal Sarcasm Explanation Generation](https://mlsanity.com/paper/2502.07391/) (2025)
- [Does Commonsense help in detecting Sarcasm?](https://mlsanity.com/paper/2109.08588/) (2021)
- [Multi-Figurative Language Generation](https://mlsanity.com/paper/2209.01835/) (2022)
- [Computational Sarcasm Analysis on Social Media: A Systematic Review](https://mlsanity.com/paper/2209.06170/) (2022)
- [How to Describe Images in a More Funny Way? Towards a Modular Approach to Cross-Modal Sarcasm Generation](https://mlsanity.com/paper/2211.10992/) (2022)
- ["When Words Fail, Emojis Prevail": Generating Sarcastic Utterances with Emoji Using Valence Reversal and Semantic Incongruity](https://mlsanity.com/paper/2305.04105/) (2023)

## Abstract (arXiv)

We propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence. Our method employs a retrieve-and-edit framework to instantiate two major characteristics of sarcasm: reversal of valence and semantic incongruity with the context which could include shared commonsense or world knowledge between the speaker and the listener. While prior works on sarcasm generation predominantly focus on context incongruity, we show that combining valence reversal and semantic incongruity based on the commonsense knowledge generates sarcasm of higher quality. Human evaluation shows that our system generates sarcasm better than human annotators 34% of the time, and better than a reinforced hybrid baseline 90% of the time.