AMBIPUN: Generating Puns with Ambiguous Context

Abstract

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 to identify unambiguous words to represent the pun and the alternative senses. We then utilize one-shot GPT3 to generate context words and then generate puns incorporating context words from both senses. Human evaluation shows that our method successfully generates puns 52% of the time, outperforming well crafted baselines and the state-of-the-art models by a large margin.