@inproceedings{luo-etal-2025-zhu,
    title = "主题感知的多意图识别与槽位填充联合建模方法",
    author = "Luo, Jing  and
      Wang, Weihua  and
      Cao, Yue  and
      Bao, Feilong  and
      Gao, Guanglai",
    editor = "Sun, Maosong  and
      Duan, Peiyong  and
      Liu, Zhiyuan  and
      Xu, Ruifeng  and
      Sun, Weiwei",
    booktitle = "Proceedings of the 24th {C}hina National Conference on Computational Linguistics ({CCL} 2025)",
    month = aug,
    year = "2025",
    address = "Jinan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/ingest-ccl/2025.ccl-1.39/",
    pages = "525--536",
    abstract = "``意图识别与槽位填充是口语理解中的两个子任务,联合建模这两项任务能够利用共享特征提升任务间的协同建模效果。然而,现有方法普遍缺乏对句子主题语义的显式建模,难以捕捉更充分的全局语义信息,尤其在多意图场景下系统建模性能下降严重。为缓解上述问题,本文提出了一种主题感知的意图识别与槽位填充联合建模方法,该方法构造了主题提取模块以学习句子主题分布表示,结合主题引导的意图和槽位表示增强网络插入主题信息,使得模型在识别句子意图和填充槽位过程中能够显式建模主题信息。实验结果表明,本文所提出方法在多意图公开数据集MixATIS和MixSNIPS上分别获得了50.9{\%}和84.8{\%}的整体准确率,相较多个基线模型取得了更优的性能表现。''"
}