2022/10/24 by Yufei Tian, Tian, Yufei, Divyanshu Sheth +3 · 3 citations
Arts and Humanities · Psychology · #Comics and Graphic Narratives #Computation and Language (cs.CL) #FOS: Computer and information sciences #Humor Studies and Applications
paper · pdf · doi:10.48550/arxiv.2210.13055
openalex publication_date 2022/10/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a unified framework to generate both homophonic and homographic puns to resolve the split-up in existing works. Specifically, we incorporate three linguistic attributes of puns to the language models: ambiguity, distinctiveness, and surprise. Our framework consists of three parts: 1) a context words/phrases selector to promote the aforementioned attributes, 2) a generation model trained on non-pun sentences to incorporate the context words/phrases into the generation output, and 3) a label predictor that learns the structure of puns which is used to steer the generation model at inference time. Evaluation results on both pun types demonstrate the efficacy of our model over strong baselines.