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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.04793 |
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| _version_ | 1866909822314610688 |
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| author | Su, Yuchen Zhu, Yonghua Wang, Ruofan Huang, Zijian Benavides-Prado, Diana Witbrock, Michael |
| author_facet | Su, Yuchen Zhu, Yonghua Wang, Ruofan Huang, Zijian Benavides-Prado, Diana Witbrock, Michael |
| contents | Pun generation seeks to creatively modify linguistic elements in text to produce humour or evoke double meanings. It also aims to preserve coherence and contextual appropriateness, making it useful in creative writing and entertainment across various media and contexts. Although pun generation has received considerable attention in computational linguistics, there is currently no dedicated survey that systematically reviews this specific area. To bridge this gap, this paper provides a comprehensive review of pun generation datasets and methods across different stages, including conventional approaches, deep learning techniques, and pre-trained language models. Additionally, we summarise both automated and human evaluation metrics used to assess the quality of pun generation. Finally, we discuss the research challenges and propose promising directions for future work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04793 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey of Pun Generation: Datasets, Evaluations and Methodologies Su, Yuchen Zhu, Yonghua Wang, Ruofan Huang, Zijian Benavides-Prado, Diana Witbrock, Michael Computation and Language Artificial Intelligence Pun generation seeks to creatively modify linguistic elements in text to produce humour or evoke double meanings. It also aims to preserve coherence and contextual appropriateness, making it useful in creative writing and entertainment across various media and contexts. Although pun generation has received considerable attention in computational linguistics, there is currently no dedicated survey that systematically reviews this specific area. To bridge this gap, this paper provides a comprehensive review of pun generation datasets and methods across different stages, including conventional approaches, deep learning techniques, and pre-trained language models. Additionally, we summarise both automated and human evaluation metrics used to assess the quality of pun generation. Finally, we discuss the research challenges and propose promising directions for future work. |
| title | A Survey of Pun Generation: Datasets, Evaluations and Methodologies |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2507.04793 |