2022/05/04 by Anirudh Mittal, Mittal, Anirudh, Yufei Tian +3 · 5 citations
Arts and Humanities · Psychology · #Artificial Intelligence (cs.AI) #Comics and Graphic Narratives #Computation and Language (cs.CL) #FOS: Computer and information sciences #Humor Studies and Applications #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2205.01825
openalex publication_date 2022/05/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.