  Humor recognition has been widely studied as a text classification problem
using data-driven approaches. However, most existing work does not examine the
actual joke mechanism to understand humor. We break down any joke into two
distinct components: the set-up and the punchline, and further explore the
special relationship between them. Inspired by the incongruity theory of humor,
we model the set-up as the part developing semantic uncertainty, and the
punchline disrupting audience expectations. With increasingly powerful language
models, we were able to feed the set-up along with the punchline into the GPT-2
language model, and calculate the uncertainty and surprisal values of the
jokes. By conducting experiments on the SemEval 2021 Task 7 dataset, we found
that these two features have better capabilities of telling jokes from
non-jokes, compared with existing baselines.
