# Pun-GAN: Generative Adversarial Network for Pun Generation

arXiv: https://arxiv.org/abs/1910.10950 · HTML: https://mlsanity.com/paper/1910.10950/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2019.

## Built on by (most cited first, 18 of 18)

- [RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge](https://mlsanity.com/paper/2101.00376/) (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)
- [ExPUNations: Augmenting Puns with Keywords and Explanations](https://mlsanity.com/paper/2210.13513/) (2022)
- [Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLP](https://mlsanity.com/paper/2104.08620/) (2021)
- [MOVER: Mask, Over-generate and Rank for Hyperbole Generation](https://mlsanity.com/paper/2109.07726/) (2021)
- [The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm Generation](https://mlsanity.com/paper/2311.05552/) (2023)
- [A Unified Framework for Pun Generation with Humor Principles](https://mlsanity.com/paper/2210.13055/) (2022)
- [OxfordTVG-HIC: Can Machine Make Humorous Captions from Images?](https://mlsanity.com/paper/2307.11636/) (2023)
- [Are LLMs Good Cryptic Crossword Solvers?](https://mlsanity.com/paper/2403.12094/) (2024)
- [A Two-Model Approach for Humour Style Recognition](https://mlsanity.com/paper/2410.12842/) (2024)
- [Context-Situated Pun Generation](https://mlsanity.com/paper/2210.13522/) (2022)
- [Who's Laughing Now? An Overview of Computational Humour Generation and Explanation](https://mlsanity.com/paper/2509.21175/) (2025)
- [A Survey of Pun Generation: Datasets, Evaluations and Methodologies](https://mlsanity.com/paper/2507.04793/) (2025)
- [Oogiri-Master: Benchmarking Humor Understanding via Oogiri](https://mlsanity.com/paper/2512.21494/) (2025)
- [Words at Play: Benchmarking Audio Pun Understanding in Large Audio-Language Models](https://mlsanity.com/paper/2603.18678/) (2026)
- [A Survey of Text Style Transfer: Applications and Ethical Implications](https://mlsanity.com/paper/2407.16737/) (2024)
- [What Makes Cryptic Crosswords Challenging for LLMs?](https://mlsanity.com/paper/2412.09012/) (2024)

## Abstract (arXiv)

In this paper, we focus on the task of generating a pun sentence given a pair of word senses. A major challenge for pun generation is the lack of large-scale pun corpus to guide the supervised learning. To remedy this, we propose an adversarial generative network for pun generation (Pun-GAN), which does not require any pun corpus. It consists of a generator to produce pun sentences, and a discriminator to distinguish between the generated pun sentences and the real sentences with specific word senses. The output of the discriminator is then used as a reward to train the generator via reinforcement learning, encouraging it to produce pun sentences that can support two word senses simultaneously. Experiments show that the proposed Pun-GAN can generate sentences that are more ambiguous and diverse in both automatic and human evaluation.