A Survey of Pun Generation: Datasets, Evaluations and Methodologies

TL;DR

This paper provides a comprehensive survey of pun generation, reviewing datasets, methods, evaluation metrics, challenges, and future directions in computational linguistics for humor creation.

Contribution

It is the first systematic review of pun generation, covering datasets, methodologies, evaluation techniques, and research challenges in the field.

Findings

Summarizes existing datasets and methods for pun generation.

Reviews automated and human evaluation metrics used.

Discusses challenges and future research directions.

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.