The Funny Thing About Incongruity: A Computational Model of Humor in Puns

Researchers showed the robot ten puns, hoping that one of them would make it laugh. Unfortunately, no pun in ten did. What makes something funny? Humor theorists posit that incongruity—perceiving a situation from different viewpoints and finding the resulting interpretations to be incompatible— contributes to sensations of mirth. In this paper, we use a computational model of sentence comprehension to formalize incongruity and test its relationship to humor in puns. By combining a noisy channel model of language comprehension and standard information theoretic measures, we derive two dimensions of incongruity—ambiguity of meaning and distinctiveness of viewpoints—and use them to predict humans’ judgments of funniness. Results showed that both ambiguity and distinctiveness are significant predictors of humor. Additionally, our model automatically identifies specific features of a pun that make it amusing. We thus show how a probabilistic model of sentence comprehension can help explain essential features of the complex phenomenon of linguistic humor.