Related papers: A Survey of Pun Generation: Datasets, Evaluations and Methodologies

A Survey of Pun Generation: Datasets, Evaluations and Methodologies

URL: http://arxiv.org/abs/2507.04793v1
Date: Mon, 07 Jul 2025 09:12:46 GMT
Title: A Survey of Pun Generation: Datasets, Evaluations and Methodologies
Authors: Yuchen Su, Yonghua Zhu, Ruofan Wang, Zijian Huang, Diana Benavides-Prado, Michael Witbrock,
Abstract summary: Pun generation seeks to creatively modify linguistic elements in text to produce humour or evoke double meanings.<n>It also aims to preserve coherence and contextual appropriateness, making it useful in creative writing and entertainment across various media and contexts.
Score: 12.556456134606565
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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