Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting Incongruity-Based Features for Humor Recognition
This work provides an incremental step in humor recognition for NLP researchers by incorporating theoretical aspects of humor into feature engineering.
This paper explores humor recognition by modeling jokes as a set-up creating semantic uncertainty and a punchline disrupting expectations. Using GPT-2, the authors calculated uncertainty and surprisal values, finding these features more effective than existing baselines for distinguishing jokes from non-jokes on the SemEval 2021 Task 7 dataset.
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.