| Challenge: | In this paper, we explore creative generation with a focus on puns. |
| Approach: | They propose an unsupervised approach to generating puns using lots of raw text and a surprisal principle. |
| Outcome: | The proposed approach generates puns 30% of the time, doubles the neural generation baseline. |
Similar Papers
A Unified Framework for Pun Generation with Humor Principles (2022.findings-emnlp)
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| Challenge: | Existing models for generating homophonic and homographic puns lack the linguistic attributes of successful puns to resolve the split-up in existing work. |
| Approach: | They propose a framework to generate both homophonic and homographic puns to resolve the split-up in existing works by incorporating three linguistic attributes of puns into the language models: ambiguity, distinctiveness, and surprise. |
| Outcome: | The proposed model over strong baselines shows that it can generate both homophonic and homographic puns. |
AmbiPun: Generating Humorous Puns with Ambiguous Context (2022.naacl-main)
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| Challenge: | Existing methods for generating homographic puns are heavy-weighted due to the lack of training data. |
| Approach: | They propose a way to generate pun sentences that does not require training on existing puns. |
| Outcome: | The proposed method outperforms baseline models and state-of-the-art models by a large margin. |
Context-Situated Pun Generation (2022.emnlp-main)
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Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Shuyang Gao, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng
| Challenge: | a new task for context-situated pun generation uses a given context to generate puns . human evaluation shows that 69% of top retrieved pun words can be used to generate context-based puns. |
| Approach: | They propose a task where puns are generated based on contextual keywords and pun words. |
| Outcome: | The proposed system generates successful puns 31% of the time given a plausible tuple of context words and pun pairs. |
A Neural Approach to Pun Generation (P18-1)
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| Challenge: | generating puns with artificial intelligence techniques requires manual training and templates. |
| Approach: | They propose neural network models for homographic pun generation that can generate puns without requiring any pun data for training. |
| Outcome: | The proposed models generate homographic puns of good readability and quality without training. |
A Survey of Pun Generation: Datasets, Evaluations and Methodologies (2025.findings-emnlp)
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| Challenge: | Pun generation aims to modify linguistic elements in text to produce humour or evoke double meanings. |
| Approach: | They propose to review pun generation datasets and methods across different stages . pun generation aims to produce humour or evoke double meanings . |
| Outcome: | This paper summarises both automated and human evaluation metrics used to assess the quality of pun generation. |
Pun-GAN: Generative Adversarial Network for Pun Generation (D19-1)
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| Challenge: | Existing methods for generating pun sentences with word senses lack large-scale corpus for supervised learning . a pun is a clever and amusing use of a word with two meanings (word senses) |
| Approach: | They propose an adversarial generative network for pun generation with a generator and a discriminator to distinguish between generated pun sentences and real sentences with specific word senses. |
| Outcome: | The proposed network generates sentences that are more ambiguous and diverse in both automatic and human evaluation. |
Homophonic Pun Generation with Lexically Constrained Rewriting (2020.emnlp-main)
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| Challenge: | a new method for generating puns using two homophones is needed to generate creative puns . early models for pun generation rely on templates and lack novelty. |
| Approach: | They propose a neural approach to generate homophonic puns with two meanings . they use constraint words to find the semantic incongruity and explicit negative constraints . |
| Outcome: | The proposed model achieves state-of-the-art in automatic and human evaluations. |
“A good pun is its own reword”: Can Large Language Models Understand Puns? (2024.emnlp-main)
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| Challenge: | Existing studies on the understanding of puns in large language models (LLMs) have not explored the use of pun in creative writing and humor creation. |
| Approach: | They propose to use pun recognition, explanation and generation tasks to evaluate the capabilities of large language models (LLMs) they adopt automated evaluation metrics from prior research and introduce new evaluation methods and metrics that align more closely with human cognition. |
| Outcome: | The proposed methods align more closely with human cognition than previous evaluation metrics. |
“Barking up the Right Tree”, a GAN-Based Pun Generation Model through Semantic Pruning (2024.lrec-main)
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| Challenge: | Existing methods for generating humorous puns are limited and require a broad spectrum of commonsense and worldly skills. |
| Approach: | They propose a GAN-based approach that employs semantic pruning and contrastive learning to generate humorous puns using a model that captures the semantic nuances of puns. |
| Outcome: | The proposed model produces semantically coherent and humorous puns while ensuring both correctness and humor. |
“I See What You Did There”: Can Large Vision-Language Models Understand Multimodal Puns? (2026.acl-long)
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Naen Xu, Jiayi Sheng, Changjiang Li, Chunyi Zhou, Yuyuan Li, Tianyu Du, Jun Wang, Zhihui Fu, Jinbao Li, Shouling Ji
| Challenge: | Puns are a common form of rhetorical wordplay that exploits polysemy and phonetic similarity to create humor. |
| Approach: | They propose a multimodal pun generation pipeline and a model to evaluate their understanding of puns. |
| Outcome: | The proposed benchmark improves the understanding of multimodal puns by 16.5% in the F1 test. |