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        <title>j10official at SemEval-2026 Task 1: Neurosymbolic Humor Generation via GTVH-Guided LLM Decomposition</title>
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            <title>Proceedings of the 20th International Workshop on Semantic Evaluation (2026)</title>
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            <namePart type="given">Ekaterina</namePart>
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    <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× 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.</abstract>
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%0 Conference Proceedings
%T j10official at SemEval-2026 Task 1: Neurosymbolic Humor Generation via GTVH-Guided LLM Decomposition
%A Agrawal, Jatin
%A Mamidi, Radhika
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F agrawal-mamidi-2026-j10official
%X 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× 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.
%U https://aclanthology.org/2026.semeval-1.379/
%P 3015-3026

Markdown (Informal)

[j10official at SemEval-2026 Task 1: Neurosymbolic Humor Generation via GTVH-Guided LLM Decomposition](https://aclanthology.org/2026.semeval-1.379/) (Agrawal & Mamidi, SemEval 2026)

• j10official at SemEval-2026 Task 1: Neurosymbolic Humor Generation via GTVH-Guided LLM Decomposition (Agrawal & Mamidi, SemEval 2026)

ACL

• Jatin Agrawal and Radhika Mamidi. 2026. j10official at SemEval-2026 Task 1: Neurosymbolic Humor Generation via GTVH-Guided LLM Decomposition. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 3015–3026, San Diego, California, USA. Association for Computational Linguistics.