AmbiPun: Generating Humorous Puns with Ambiguous Context

Anirudh Mittal; Yufei Tian; Nanyun Peng

NAACL 2022long29 citations

AmbiPun: Generating Humorous Puns with Ambiguous Context

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.

BibTeX

@inproceedings{mittal-etal-2022-ambipun,
    title = "{A}mbi{P}un: Generating Humorous Puns with Ambiguous Context",
    author = "Mittal, Anirudh  and
      Tian, Yufei  and
      Peng, Nanyun",
    editor = "Carpuat, Marine  and
      de Marneffe, Marie-Catherine  and
      Meza Ruiz, Ivan Vladimir",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-main.77/",
    doi = "10.18653/v1/2022.naacl-main.77",
    pages = "1053--1062"
}