Who’s Laughing Now? An Overview of Computational Humour Generation and Explanation

Tyler Loakman, William Thorne, Chenghua Lin


Abstract
The creation and perception of humour is a fundamental human trait, positioning its computational understanding as one of the most challenging tasks in natural language processing (NLP). As an abstract, creative, and frequently context-dependent construct, humour requires extensive reasoning to understand and create, making it a pertinent task for assessing the common-sense knowledge and reasoning abilities of modern large language models (LLMs). In this work, we survey the landscape of computational humour as it pertains to the generative tasks of creation and explanation. We observe that, despite the task of understanding humour bearing all the hallmarks of a foundational NLP task, work on generating and explaining humour beyond puns remains sparse, while state-of-the-art models continue to fall short of human capabilities. We bookend our literature survey by motivating the importance of computational humour processing as a subdiscipline of NLP and presenting an extensive discussion of future directions for research in the area that takes into account the subjective and ethically ambiguous nature of humour.
Anthology ID:
2025.inlg-main.45
Volume:
Proceedings of the 18th International Natural Language Generation Conference
Month:
October
Year:
2025
Address:
Hanoi, Vietnam
Editors:
Lucie Flek, Shashi Narayan, Lê Hồng Phương, Jiahuan Pei
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
780–794
Language:
URL:
https://preview.aclanthology.org/author-page-you-zhang-rochester/2025.inlg-main.45/
DOI:
Bibkey:
Cite (ACL):
Tyler Loakman, William Thorne, and Chenghua Lin. 2025. Who’s Laughing Now? An Overview of Computational Humour Generation and Explanation. In Proceedings of the 18th International Natural Language Generation Conference, pages 780–794, Hanoi, Vietnam. Association for Computational Linguistics.
Cite (Informal):
Who’s Laughing Now? An Overview of Computational Humour Generation and Explanation (Loakman et al., INLG 2025)
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PDF:
https://preview.aclanthology.org/author-page-you-zhang-rochester/2025.inlg-main.45.pdf