--- title: "Humor Generation – Text-based Humor Generation (English)" description: "The paper describes a system for SemEval-2026 Task 1 (MWAHAHA), Subtask A. In this English… 8 references, cited by 1 paper here." authors: ["Hemeshkumar Parthiban", "Priyadharsini R"] year: 2026 venue: "Proceedings of the 20th International Workshop on Semantic Evaluation (2026)" source: "https://aclanthology.org/2026.semeval-1.104/" doi: "10.18653/v1/2026.semeval-1.104" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/parthiban-2026-humor-generation-text-based-humor/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/parthiban-2026-humor-generation-text-based-humor/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/13/) / Humor Generation – Text-based Humor Generation (English) # Humor Generation – Text-based Humor Generation (English) **Authors:** Hemeshkumar Parthiban and Priyadharsini R · **Year:** 2026 · **Venue:** [Proceedings of the 20th International Workshop on Semantic Evaluation (2026)](https://md.research.elamur.ai/free/en/venues/semeval/) ([SemEval 2026](https://md.research.elamur.ai/free/en/venues/semeval/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2026.semeval-1.104/) · [PDF](https://aclanthology.org/2026.semeval-1.104.pdf) · [DOI 10.18653/v1/2026.semeval-1.104](https://doi.org/10.18653/v1/2026.semeval-1.104) · [OpenAlex](https://openalex.org/W7166850339) **Citations:** 0 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read ## Summary The paper describes a system for SemEval-2026 Task 1 (MWAHAHA), Subtask A. In this English humor-generation task, a model gets either a news headline or a pair of unusual words that must both appear, and it must produce an original, safe joke. The authors use no training. Their pipeline has four steps: light input cleanup, a single fixed instruction template, zero-shot inference with a GPT-4–class model under deterministic decoding, and an automatic check that regenerates outputs that break the format rules. The template asks for one sentence of 8–12 words in a deadpan, understated style that gently undercuts expectations and avoids loud punchlines or unsafe content. On the human-judged leaderboard, the system finished in the middle of the field. A qualitative ablation suggests that fuller constraints give more stylistically fitting output. The work shows that prompt-only control is a cheap, reproducible baseline for constrained creative generation. However, the evidence for quality improvements is limited. ## Key points - **Task inputs:** Subtask A has two settings, Word Inclusion (two rare words that must both be used) and News Headline (a satirical sentence based on a real headline). Outputs are ranked by human comparative judgment. - **Leaderboard result:** The submission "hemeshkumar_31" scored 843 (95% CI [802, 875]) and placed 31st. Top systems scored above 1000. - **Relative performance claim:** The authors say this is about 84% of the top score, but that figure assumes a top score of roughly 1000. - **Prompt ablation:** Three prompts were compared (unconstrained, tone-only, full constraints). The comparison was qualitative only, with no numeric scores reported. - **Error analysis:** Outputs were sometimes well-formed but not funny, generic in their satire, overly literal, or awkwardly compressed by the length limit. Suggested future work includes adaptive prompts, fine-tuning, and incongruity modeling. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* ## Institutions Also on the byline: [Rajalakshmi Engineering College](https://ror.org/01dw2vm55). 📚 Shelves (6): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works · [💭 Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/) · 1,649 works · [💭 Social Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/social-psychology/) · 330 works 🧭 Topics (1): [English](https://md.research.elamur.ai/free/en/topics/english/) · 2,379 works ## Cite Cite as: Hemeshkumar Parthiban and Priyadharsini R. 2026. Humor Generation – Text-based Humor Generation (English). Proceedings of the 20th International Workshop on Semantic Evaluation (2026). The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{parthiban2026humor, title = {{Humor Generation – Text-based Humor Generation (English)}}, author = {Hemeshkumar Parthiban and Priyadharsini R}, booktitle = {Proceedings of the 20th International Workshop on Semantic Evaluation (2026)}, year = {2026}, doi = {10.18653/v1/2026.semeval-1.104}, url = {https://doi.org/10.18653/v1/2026.semeval-1.104} } ``` - [← previous HumMusQA: A Human-written Music Understanding QA Benchmark Dataset](https://md.research.elamur.ai/free/en/papers/weck-2026-hummusqa-human-written-music-understanding/) - [next → Hunyuan-A13B Technical Report](https://md.research.elamur.ai/free/en/papers/team-2026-hunyuan-hunyuan-a13b-technical-report/) - [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/13/) ## References 8 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Tom Brown and 1 others. 2020. [Language models are few-shot learners](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/). Advances in Neural Information Processing Systems. 2. Md Kamrul Hasan and 1 others. 2019. Ur-funny: A multimodal language dataset for understanding humor. In Proceedings of EMNLP. [find](https://scholar.google.com/scholar?q=Md+Kamrul+Hasan+and+1+others%2E+2019%2E+Ur%2Dfunny%3A+A+multimodal+language+dataset+for+understanding+humor%2E+In+Proceedings+of+EMNLP%2E) 3. Pengfei Liu and 1 others. 2023. Pre-train, prompt, and predict: A systematic survey of prompting methods. ACM Computing Surveys. [find](https://scholar.google.com/scholar?q=Pengfei+Liu+and+1+others%2E+2023%2E+Pre%2Dtrain%2C+prompt%2C+and+predict%3A+A+systematic+survey+of+prompting+methods%2E+ACM+Computing+Surveys%2E) 4. Xiaotong Liu, Carlos Fernandez, and Rohan Mehta. 2025. Constraint-guided generation in large language models. In Proceedings of the 2025 Annual Meeting of the Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Xiaotong+Liu%2C+Carlos+Fernandez%2C+and+Rohan+Mehta%2E+2025%2E+Constraint%2Dguided+generation+in+large+language+models%2E+In+Proceedings+of+the+2025+Annual+Meeting+of+the+Association+for+Computational+Linguistics) 5. Rada Mihalcea and Carlo Strapparava. 2005. [Making computers laugh: Investigations in automatic humor recognition](https://md.research.elamur.ai/free/en/papers/mihalcea-2005-making-computers-laugh-investigations-automatic/). In Proceedings of Human Language Technology Conference. 6. Task Organizers. 2026. Semeval-2026 task 1: Mwahaha – competition on humor generation. In Proceedings of the 20th International Workshop on Semantic Evaluation. Placeholder citation; official BibTeX to be released. [find](https://scholar.google.com/scholar?q=Task+Organizers%2E+2026%2E+Semeval%2D2026+task+1%3A+Mwahaha+%E2%80%93+competition+on+humor+generation%2E+In+Proceedings+of+the+20th+International+Workshop+on+Semantic+Evaluation%2E+Placeholder+citation%3B+official+BibTeX+t) 7. Jun Wang, Aarav Patel, and Priya Singh. 2024. Evaluating large language models for creative text generation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. [find](https://scholar.google.com/scholar?q=Jun+Wang%2C+Aarav+Patel%2C+and+Priya+Singh%2E+2024%2E+Evaluating+large+language+models+for+creative+text+generation%2E+In+Proceedings+of+the+2024+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2E) 8. Rui Zhang, Yifan Li, and Ming Chen. 2024. Controlled text generation with [large language models: A survey](https://md.research.elamur.ai/free/en/papers/minaee-2024-large-language-models-survey/). Transactions of the Association for Computational Linguistics. *Updated 31 October 2026*