# "The Boating Store Had Its Best Sail Ever": Pronunciation-attentive Contextualized Pun Recognition

arXiv: https://arxiv.org/abs/2004.14457 · HTML: https://mlsanity.com/paper/2004.14457/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2020.

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

- [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)
- [Creating a Lens of Chinese Culture: A Multimodal Dataset for Chinese Pun Rebus Art Understanding](https://mlsanity.com/paper/2406.10318/) (2024)
- [On the Wings of Imagination: Conflicting Script-based Multi-role Framework for Humor Caption Generation](https://mlsanity.com/paper/2602.06423/) (2026)
- ["I See What You Did There": Can Large Vision-Language Models Understand Multimodal Puns?](https://mlsanity.com/paper/2604.05930/) (2026)
- [Humor@IITK at SemEval-2021 Task 7: Large Language Models for Quantifying Humor and Offensiveness](https://mlsanity.com/paper/2104.00933/) (2021)
- [Words at Play: Benchmarking Audio Pun Understanding in Large Audio-Language Models](https://mlsanity.com/paper/2603.18678/) (2026)
- [A Dual-Attention Neural Network for Pun Location and Using Pun-Gloss Pairs for Interpretation](https://mlsanity.com/paper/2110.07209/) (2021)
- [Phun-Bench: Evaluating LLMs on Phonological Understanding in Chinese](https://mlsanity.com/paper/2606.07300/) (2026)

## Abstract (arXiv)

Humor plays an important role in human languages and it is essential to model humor when building intelligence systems. Among different forms of humor, puns perform wordplay for humorous effects by employing words with double entendre and high phonetic similarity. However, identifying and modeling puns are challenging as puns usually involved implicit semantic or phonological tricks. In this paper, we propose Pronunciation-attentive Contextualized Pun Recognition (PCPR) to perceive human humor, detect if a sentence contains puns and locate them in the sentence. PCPR derives contextualized representation for each word in a sentence by capturing the association between the surrounding context and its corresponding phonetic symbols. Extensive experiments are conducted on two benchmark datasets. Results demonstrate that the proposed approach significantly outperforms the state-of-the-art methods in pun detection and location tasks. In-depth analyses verify the effectiveness and robustness of PCPR.