@inproceedings{agrawal-mamidi-2026-j10official,
    title = "j10official at {S}em{E}val-2026 Task 1: Neurosymbolic Humor Generation via {GTVH}-Guided {LLM} Decomposition",
    author = "Agrawal, Jatin  and
      Mamidi, Radhika",
    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-1.379/",
    pages = "3015--3026",
    ISBN = "979-8-89176-414-9",
    abstract = "We present a neurosymbolic pipeline for computational humor generation grounded in the General Theory of Verbal Humor. The system constructs the joke in five sequential stages: context analysis, humor architecture (identifying core incongruity), delivery strategy, content writing, and pairwise judging, orchestrated through the DSPy framework. The system generates four candidate jokes per input with independent humor strategies, then selects the best through knockout tournament-style evaluation. Despite using Gemma 3 27B, a model with roughly 20{\texttimes} fewer total parameters than frontier systems, our approach achieves competitive results across all five subtasks of SemEval- 2026 Task 1 (MWAHAHA), placing 2nd in two subtasks. We argue that these results demonstrate the viability of structured, theory-driven decomposition for solving complex tasks and that how a model reasons about humor is just as important as how large the model is."
}