  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.
