@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://preview.aclanthology.org/ingest-acl-workshops/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."
}