@inproceedings{shuaishuai-etal-2024-ji,
    title = "基于联邦知识蒸馏的跨语言社交媒体事件检测(Cross-Lingual Social Event Detection Based on Federated Knowledge Distillation)",
    author = "Zhou, Shuaishuai  and
      Zhu, Enchang  and
      Gao, Shengxiang  and
      Yu, Zhengtao  and
      Xian, Yantuan  and
      Zhao, Zixiao  and
      Chen, Lin",
    editor = "Maosong, Sun  and
      Jiye, Liang  and
      Xianpei, Han  and
      Zhiyuan, Liu  and
      Yulan, He",
    booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)",
    month = jul,
    year = "2024",
    address = "Taiyuan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/author-degibert/2024.ccl-1.36/",
    pages = "467--480",
    language = "zho",
    abstract = "``社交媒体事件检测是指在从各类社交媒体的内容中挖掘热点事件。在实际情况中,由于数据稀缺,社交媒体事件检测在低资源的情况下表现较差。现有的方法主要通过跨语言知识迁移等方式来缓解低资源问题,但忽略了数据隐私问题。因此,本文提出了基于联邦知识蒸馏的跨语言社交媒体事件检测框架(FedEvent),旨在将富资源客户端知识蒸馏到低资源客户端。该框架通过结合参数高效微调技术和三组对比损失,实现非英文语义空间到英文语义空间的有效映射,并采用联邦蒸馏策略,保障数据隐私的前提下实现知识的迁移。此外,我们还设计了一套四阶段生命周期机制以适应增量场景。最后,我们在真实数据集上进行实验以证明该框架的有效性。''"
}