# ExPUNations: Augmenting Puns with Keywords and Explanations

arXiv: https://arxiv.org/abs/2210.13513 · HTML: https://mlsanity.com/paper/2210.13513/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 52 references that are in the map.
- First posted on arXiv 24 Oct 2022.

## Builds on (most cited first, 27 of 27)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems](https://mlsanity.com/paper/1705.04146/) (2017)
- [Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?](https://mlsanity.com/paper/2004.03685/) (2020)
- [e-SNLI: Natural Language Inference with Natural Language Explanations](https://mlsanity.com/paper/1812.01193/) (2018)
- [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020)
- [Explain Yourself! Leveraging Language Models for Commonsense Reasoning](https://mlsanity.com/paper/1906.02361/) (2019)
- [Measuring Association Between Labels and Free-Text Rationales](https://mlsanity.com/paper/2010.12762/) (2020)
- [UR-FUNNY: A Multimodal Language Dataset for Understanding Humor](https://mlsanity.com/paper/1904.06618/) (2019)
- [NILE : Natural Language Inference with Faithful Natural Language Explanations](https://mlsanity.com/paper/2005.12116/) (2020)
- [e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks](https://mlsanity.com/paper/2105.03761/) (2021)
- [A Retrieve-and-Edit Framework for Predicting Structured Outputs](https://mlsanity.com/paper/1812.01194/) (2018)
- [WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-Hop Inference](https://mlsanity.com/paper/1802.03052/) (2018)
- [Humor Detection: A Transformer Gets the Last Laugh](https://mlsanity.com/paper/1909.00252/) (2019)
- [Pun Generation with Surprise](https://mlsanity.com/paper/1904.06828/) (2019)
- [A Study of Automatic Metrics for the Evaluation of Natural Language Explanations](https://mlsanity.com/paper/2103.08545/) (2021)
- [Towards Interpretable Natural Language Understanding with Explanations as Latent Variables](https://mlsanity.com/paper/2011.05268/) (2020)
- [TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing](https://mlsanity.com/paper/2002.12620/) (2020)
- [AmbiPun: Generating Humorous Puns with Ambiguous Context](https://mlsanity.com/paper/2205.01825/) (2022)
- [Pun-GAN: Generative Adversarial Network for Pun Generation](https://mlsanity.com/paper/1910.10950/) (2019)
- ["So You Think You're Funny?": Rating the Humour Quotient in Standup Comedy](https://mlsanity.com/paper/2110.12765/) (2021)
- [Joint Detection and Location of English Puns](https://mlsanity.com/paper/1909.00175/) (2019)
- ["The Boating Store Had Its Best Sail Ever": Pronunciation-attentive Contextualized Pun Recognition](https://mlsanity.com/paper/2004.14457/) (2020)
- ["Judge me by my size (noun), do you?'' YodaLib: A Demographic-Aware Humor Generation Framework](https://mlsanity.com/paper/2006.00578/) (2020)

## Built on by (most cited first, 21 of 21)

- ["A good pun is its own reword": Can Large Language Models Understand Puns?](https://mlsanity.com/paper/2404.13599/) (2024)
- [Crowd Score: A Method for the Evaluation of Jokes using Large Language Model AI Voters as Judges](https://mlsanity.com/paper/2212.11214/) (2022)
- [Evaluating Human Alignment and Model Faithfulness of LLM Rationale](https://mlsanity.com/paper/2407.00219/) (2024)
- [Can Language Models Laugh at YouTube Short-form Videos?](https://mlsanity.com/paper/2310.14159/) (2023)
- [The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm Generation](https://mlsanity.com/paper/2311.05552/) (2023)
- [Creating a Lens of Chinese Culture: A Multimodal Dataset for Chinese Pun Rebus Art Understanding](https://mlsanity.com/paper/2406.10318/) (2024)
- [Humor Mechanics: Advancing Humor Generation with Multistep Reasoning](https://mlsanity.com/paper/2405.07280/) (2024)
- [Comparing Apples to Oranges: A Dataset & Analysis of LLM Humour Understanding from Traditional Puns to Topical Jokes](https://mlsanity.com/paper/2507.13335/) (2025)
- [GODBench: A Benchmark for Multimodal Large Language Models in Video Comment Art](https://mlsanity.com/paper/2505.11436/) (2025)
- [Chumor 1.0: A Truly Funny and Challenging Chinese Humor Understanding Dataset from Ruo Zhi Ba](https://mlsanity.com/paper/2406.12754/) (2024)
- [Pun Unintended: LLMs and the Illusion of Humor Understanding](https://mlsanity.com/paper/2509.12158/) (2025)
- [HotComment: A Benchmark for Evaluating Popularity of Online Comments](https://mlsanity.com/paper/2604.25614/) (2026)
- [REFFLY: Melody-Constrained Lyrics Editing Model](https://mlsanity.com/paper/2409.00292/) (2024)
- [A Survey of Pun Generation: Datasets, Evaluations and Methodologies](https://mlsanity.com/paper/2507.04793/) (2025)
- ["I See What You Did There": Can Large Vision-Language Models Understand Multimodal Puns?](https://mlsanity.com/paper/2604.05930/) (2026)
- [Pun Intended: Multi-Agent Translation of Wordplay with Contrastive Learning and Phonetic-Semantic Embeddings for CLEF JOKER 2025 Task 2](https://mlsanity.com/paper/2507.06506/) (2025)
- [Words at Play: Benchmarking Audio Pun Understanding in Large Audio-Language Models](https://mlsanity.com/paper/2603.18678/) (2026)
- [KoWit-24: A Richly Annotated Dataset of Wordplay in News Headlines](https://mlsanity.com/paper/2503.01510/) (2025)
- [lmfaoooo at SemEval-2026 Task 1: Humor Is an Audience. Preference Modeling for Constrained Humor Generation](https://mlsanity.com/paper/2606.00022/) (2026)
- [Towards High-Level Semantic Intelligence](https://mlsanity.com/paper/2607.24082/) (2026)
- [Pun Intended: Multi-Agent Translation of Wordplay with Contrastive Learning and Phonetic-Semantic Embeddings](https://mlsanity.com/paper/2608.04311/) (2026)

## Abstract (arXiv)

The tasks of humor understanding and generation are challenging and subjective even for humans, requiring commonsense and real-world knowledge to master. Puns, in particular, add the challenge of fusing that knowledge with the ability to interpret lexical-semantic ambiguity. In this paper, we present the ExPUNations (ExPUN) dataset, in which we augment an existing dataset of puns with detailed crowdsourced annotations of keywords denoting the most distinctive words that make the text funny, pun explanations describing why the text is funny, and fine-grained funniness ratings. This is the first humor dataset with such extensive and fine-grained annotations specifically for puns. Based on these annotations, we propose two tasks: explanation generation to aid with pun classification and keyword-conditioned pun generation, to challenge the current state-of-the-art natural language understanding and generation models' ability to understand and generate humor. We showcase that the annotated keywords we collect are helpful for generating better novel humorous texts in human evaluation, and that our natural language explanations can be leveraged to improve both the accuracy and robustness of humor classifiers.