# Joint Detection and Location of English Puns

arXiv: https://arxiv.org/abs/1909.00175 · HTML: https://mlsanity.com/paper/1909.00175/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2019.

## Built on by (most cited first, 9 of 9)

- [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)
- ["A good pun is its own reword": Can Large Language Models Understand Puns?](https://mlsanity.com/paper/2404.13599/) (2024)
- [BRAINTEASER: Lateral Thinking Puzzles for Large Language Models](https://mlsanity.com/paper/2310.05057/) (2023)
- [Weak-eval-Strong: Evaluating and Eliciting Lateral Thinking of LLMs with Situation Puzzles](https://mlsanity.com/paper/2410.06733/) (2024)
- [On the Wings of Imagination: Conflicting Script-based Multi-role Framework for Humor Caption Generation](https://mlsanity.com/paper/2602.06423/) (2026)
- [When a Computer Cracks a Joke: Automated Generation of Humorous Headlines](https://mlsanity.com/paper/2109.08702/) (2021)
- ["I See What You Did There": Can Large Vision-Language Models Understand Multimodal Puns?](https://mlsanity.com/paper/2604.05930/) (2026)
- [A Dual-Attention Neural Network for Pun Location and Using Pun-Gloss Pairs for Interpretation](https://mlsanity.com/paper/2110.07209/) (2021)

## Abstract (arXiv)

A pun is a form of wordplay for an intended humorous or rhetorical effect, where a word suggests two or more meanings by exploiting polysemy (homographic pun) or phonological similarity to another word (heterographic pun). This paper presents an approach that addresses pun detection and pun location jointly from a sequence labeling perspective. We employ a new tagging scheme such that the model is capable of performing such a joint task, where useful structural information can be properly captured. We show that our proposed model is effective in handling both homographic and heterographic puns. Empirical results on the benchmark datasets demonstrate that our approach can achieve new state-of-the-art results.