@inproceedings{shen-etal-2026-ict,
title = "{ICT}-{NLP} at {S}em{E}val-2026 Task 1: Humor Generation via {RAG}-based Augmentation and Multi-{LLM} Internal-External Voting",
author = "Shen, Wutao and
Huang, Liyuan and
He, Jiawei and
Li, Lin and
Zhang, Jin",
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://aclanthology.org/2026.semeval-1.247/",
pages = "1965--1972",
ISBN = "979-8-89176-414-9",
abstract = "This paper presents the system we developed for SemEval-2026 Task 1: Humor Generation. The task focuses on developing systems capable of generating genuinely humorous content under various constraints. In this work, we propose using a Retrieval-Augmented Generation approach to preprocess news headlines and obtain summaries of news content. Furthermore, we employ a unified humor generation mode to adapt to the two types of generation constraints. Finally, we conduct an internal-external voting process to produce the final optimal joke output. Our approach achieves competitive performance in this task: it ranks 1st (tied) among all participating teams in the Chinese track of Subtask A."
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<abstract>This paper presents the system we developed for SemEval-2026 Task 1: Humor Generation. The task focuses on developing systems capable of generating genuinely humorous content under various constraints. In this work, we propose using a Retrieval-Augmented Generation approach to preprocess news headlines and obtain summaries of news content. Furthermore, we employ a unified humor generation mode to adapt to the two types of generation constraints. Finally, we conduct an internal-external voting process to produce the final optimal joke output. Our approach achieves competitive performance in this task: it ranks 1st (tied) among all participating teams in the Chinese track of Subtask A.</abstract>
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%0 Conference Proceedings
%T ICT-NLP at SemEval-2026 Task 1: Humor Generation via RAG-based Augmentation and Multi-LLM Internal-External Voting
%A Shen, Wutao
%A Huang, Liyuan
%A He, Jiawei
%A Li, Lin
%A Zhang, Jin
%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 shen-etal-2026-ict
%X This paper presents the system we developed for SemEval-2026 Task 1: Humor Generation. The task focuses on developing systems capable of generating genuinely humorous content under various constraints. In this work, we propose using a Retrieval-Augmented Generation approach to preprocess news headlines and obtain summaries of news content. Furthermore, we employ a unified humor generation mode to adapt to the two types of generation constraints. Finally, we conduct an internal-external voting process to produce the final optimal joke output. Our approach achieves competitive performance in this task: it ranks 1st (tied) among all participating teams in the Chinese track of Subtask A.
%U https://aclanthology.org/2026.semeval-1.247/
%P 1965-1972
Markdown (Informal)
[ICT-NLP at SemEval-2026 Task 1: Humor Generation via RAG-based Augmentation and Multi-LLM Internal-External Voting](https://aclanthology.org/2026.semeval-1.247/) (Shen et al., SemEval 2026)
ACL