AmbiPun: Generating Humorous Puns with Ambiguous Context

Anirudh Mittal, Yufei Tian, Nanyun Peng


Abstract
In this paper, we propose a simple yet effective way to generate pun sentences that does not require any training on existing puns. Our approach is inspired by humor theories that ambiguity comes from the context rather than the pun word itself. Given a pair of definitions of a pun word, our model first produces a list of related concepts through a reverse dictionary. We then utilize one-shot GPT3 to generate context words and then generate puns incorporating context words from both concepts. Human evaluation shows that our method successfully generates pun 52% of the time, outperforming well-crafted baselines and the state-of-the-art models by a large margin.
Anthology ID:
2022.naacl-main.77
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1053–1062
Language:
URL:
https://aclanthology.org/2022.naacl-main.77
DOI:
10.18653/v1/2022.naacl-main.77
Bibkey:
Cite (ACL):
Anirudh Mittal, Yufei Tian, and Nanyun Peng. 2022. AmbiPun: Generating Humorous Puns with Ambiguous Context. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1053–1062, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
AmbiPun: Generating Humorous Puns with Ambiguous Context (Mittal et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.77.pdf
Code
 pluslabnlp/ambipun
Data
Billion Word Benchmark