@inproceedings{amir-etal-2026-navigating,
    title = "Navigating the Joke Space: Towards Automated Originality Assessment of {AI}-Generated Humor",
    author = "Amir, Ori  and
      Ngo, Huyen  and
      Toplyn, Joe  and
      Hickerson, Kevin",
    editor = "Amir, Ori  and
      Hempelmann, Christian F.  and
      Rayz, Julia  and
      Dong, Tiansi  and
      Miller, Tristan",
    booktitle = "Proceedings of the 2nd Workshop on Computational Humor ({CH}um 2026)",
    month = jul,
    year = "2026",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.chum-1.8/",
    pages = "95--105",
    ISBN = "979-8-89176-431-6",
    abstract = "This study validates automated, corpus-based methods for quantifying joke originality using ``topic handles'' {---} key nouns or noun phrases capturing a joke{'}s script opposition and logical mechanism (per the General Theory of Verbal Humor). Using a reference corpus of one million jokes in English from Reddit, we compute Pointwise Mutual Information (PMI) in three variants (raw co-occurrence, semantic-cluster smoothing, and word-decomposition) and two embedding-based measures (handle-level conceptual distance and full-text corpus novelty via Sentence-BERT). We evaluate these measures on 400 LLM-generated jokes (200 each from GPT-4o and GPT-5.4) and 80 jokes from the Witscript-powered JEST benchmark, rated by three professional comedians for originality and funniness. Corpus novelty and concept distance between the most semantically distant handle pair both correlated significantly with human originality ratings ($\rho$ = .37); PMI-based measures showed weaker but significant associations ($\rho$ = .23{--}.25) on the most original handle pair. A Lasso-based composite of the three strongest predictors achieved $\rho$ = .40 (cross-validated), capturing 82{\%} of the theoretically predictable variance given inter-rater agreement. These results demonstrate that handle-based PMI and semantic novelty metrics offer practical, quantitative tools for assessing originality in AI-generated humor, advancing objective evaluation of computational creativity."
}