A Survey of Pun Generation: Datasets, Evaluations and Methodologies (2025)

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. This field has been widely studied in computational linguistics, while there are currently no surveys that specifically focus on pun generation. To bridge this gap, this paper provides a comprehensive review of pun generation datasets and methods across different stages, including traditional 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.

## introduction a pun is a kind of rhetorical style that leverages the polysemy or phonetic similarity of words to produce expressions with double or multiple meanings @xcite . beyond mere wordplay, puns serve as a crucial mechanism of linguistic creativity, enriching communication and making it more engaging @xcite . for example, the pun sentence "i used to be a banker, but i lost interest" plays on the pun words "interest", encompassing both a lack of enthusiasm for banking as a profession and the idea of financial loss. this ability to encode multiple layers of meaning fosters cognitive flexibility, encouraging individuals to interpret language in innovative ways @xcite . due to the unique capacity of puns, they are widely used in advertising @xcite , literature @xcite , and various other fields. natural language generation (nlg) tasks involve the creation of human-like text by computers based on given data or input @xcite , with pun generation being a notable and challenging aspect of such tasks. there are various approaches utilised in automatic pun generation, including template-based methods @xcite , deep neural network approaches @xcite , and pre-trained language models (plms) employing various training and inference styles @xcite . these methods are applied to different types of puns, with a particular focus on homophonic @xcite , homographic @xcite , heterographic puns @xcite and visual puns @xcite . despite the long-standing research interest in pun generation, a comprehensive literature review in this field has not been conducted, to the best of our knowledge. some existing relevant surveys focus on generating creative writing and explore tasks such as poetry composition @xcite , storytelling @xcite , arts @xcite and metaphor @xcite . it is noteworthy that @xcite outlined methods to humour generation, discussing various systems based on templates and neural networks, along with their respective strengths and weaknesses. however, they did not cover the pun research nor incorporate relevant technologies associated with large language models (llms). therefore, we aim to address this gap by conducting the first comprehensive survey on pun generation, which can provide valuable guidance for researchers engaged in the study of puns. in this survey, we review the past three decades of research and examine the current state of natural language pun generation, analysing the datasets and categorising these methods in five groups based on their technological development timeline: (1) conventional methods, which involve generating puns by manually or automatically constructing templates; (2) classic deep neural networks (dnns), leveraging architectures, such as rnns and their variants, to learn pun patterns from data; overall, the paper is organised as follows: section 2 reviews the main categories of puns and provides examples for each category. section 3, 4 and 5 summarise the relevant datasets, methods, and evaluation metrics, as shown in figure 1. we also discuss the challenges and outline future research directions in section 6, as well as conclude with final remarks in section 7. ## pun categories this section outlines the main four types of puns: i) homophonic puns, ii) heterographic puns, iii) homographic puns and iv) visual pun. ## dataset in this section, we present the current datasets that have been used and constructed for pun research. we classified the datasets into generic datasets, derived datasets and human-annotated datasets. for the detailed table of the pun dataset, please refer to appendix c. ## methodology in this section, we provide an overview of existing approaches to pun generation. ## evaluation strategies in this section, we examine both automatic and human evaluation methods for pun generation. ## challenges and future directions this section outlines the challenges and explores potential directions for future work. ## conclusion in this paper, we present a comprehensive survey on pun generation tasks, including phonetic, graphic and visual puns. we classify and thoughly analyse the datasets used in pun research, review previous approaches to pun generation, discuss existing methods, as well as summarize the evaluation metrics for pun generation. furthermore, we highlight the challenges and future directions, offering insights for researchers interested in pun generation. to enhance the research, we plan to provide an updated reading list available on the github repository.