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.

通过研究幽默机制，将笑话分成两个部分：设定和妙语。由于底层模型的不断升级，我们能够将设置和妙语一起馈入 GPT-2 语言模型，并计算笑话的不确定性和惊喜度。实验结果表明，这两个特征具有更好的识别非笑话和笑话的能力。