@inproceedings{chen-etal-2022-rong,
    title = "融合知识的多目标词联合框架语义分析模型(Knowledge-integrated Joint Model For Multi-target Frame Semantic Parsing)",
    author = "Chen, Xudong  and
      Zheng, Ce  and
      Chang, Baobao",
    editor = "Sun, Maosong  and
      Liu, Yang  and
      Che, Wanxiang  and
      Feng, Yang  and
      Qiu, Xipeng  and
      Rao, Gaoqi  and
      Chen, Yubo",
    booktitle = "Proceedings of the 21st Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Nanchang, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/2022.ccl-1.13/",
    pages = "132--142",
    language = "zho",
    abstract = "{\textquotedblleft}框架语义分析任务是自然语言处理领域的一项基础性任务。先前的研究工作大多针对单目标词进行模型设计,无法一次性完成多个目标词的框架语义结构提取。本文提出了一个面向多目标的框架语义分析模型,实现对多目标词的联合预测。该模型对框架语义分析的各项子任务进行交互性建模,实现子任务间的双向交互。此外,本文利用关系图网络对框架关系信息进行编码,将其作为框架语义学知识融入模型中。实验表明,本文模型在不借助额外语料的情况下相比之前模型都有不同程度的提高。消融实验证明了本文模型设计的有效性。此外我们分析了模型目前存在的局限性以及未来的改进方向。{\textquotedblright}"
}