@inproceedings{tan-etal-2023-ccl23,
    title = "{CCL}23-Eval任务4系统报告:基于深度学习的空间语义理解(System Report for {CCL}23-Eval Task4:Spatial Semantic Understanding Based on Deep Learning.)",
    author = "Tan, ChenKun  and
      Hu, XianNian  and
      Qiu, XinPeng",
    editor = "Sun, Maosong  and
      Qin, Bing  and
      Qiu, Xipeng  and
      Jiang, Jing  and
      Han, Xianpei",
    booktitle = "Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)",
    month = aug,
    year = "2023",
    address = "Harbin, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.ccl-3.13/",
    pages = "139--149",
    language = "zho",
    abstract = "{\textquotedblleft}本文介绍了参赛系统在第三届中文空间语义理解评测(SpaCE2023)采用的技术路线:面向空间语义异常识别任务提出了抽取方法,并结合生成器进一步完成了空间语义角色标注任务,空间场景异同判断任务则使用了大语言模型生成。本文进一步探索了大语言模型在评测数据集上的应用,发现指令设计是未来工作的重点和难点。参赛系统的代码和模型见https://github.com/ShacklesLay/Space2023。{\textquotedblright}"
}