@inproceedings{schechter-etal-2026-jct,
    title = "{JCT} at {S}em{E}val-2026 Task 1: Let the Best Joke Win - A Generate - and-Rank Approach to Constrained Humor",
    author = "Schechter, Batya  and
      Barzel, Sarah  and
      Liebeskind, Chaya",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.13/",
    pages = "89--94",
    ISBN = "979-8-89176-414-9",
    abstract = "We present a humor generation system forSemEval-2026 Task 1, Subtask A (Castro et al.,2026) that produces short jokes under lexicalor headline-based constraints. For each input,our system generates multiple candidate jokesusing a large language model across diverse hu-mor styles and prompting strategies, includingzero-shot, few-shot, and structured prompting.Constraint satisfaction is explicitly enforced,either by requiring exact lexical inclusion orby approximating semantic relevance to a head-line using sentence-embedding similarity. Allvalid candidates are ranked using a weightedhumor score that combines semantic incon-gruity, emotion-based humor potential, ironylikelihood, linguistic fluency, and novelty withrespect to a large external jokes corpus, andthe single highest-scoring joke is selected foreach constraint. This approach follows a best-candidate selection paradigm, leveraging auto-mated humor proxies to improve joke qualitywithout task-specific fine-tuning."
}