AmbiPun: Generating Humorous Puns with Ambiguous Context

By Anirudh Mittal, Yufei Tian, Nanyun Peng. 2022

36 citations recorded by the source corpus.

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.

Subfields: Social Psychology, Visual Arts and Performing Arts