@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://aclanthology.org/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。''"
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<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。”</abstract>
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%0 Conference Proceedings
%T 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)
%A Zhou, Shuaishuai
%A Zhu, Enchang
%A Gao, Shengxiang
%A Yu, Zhengtao
%A Xian, Yantuan
%A Zhao, Zixiao
%A Chen, Lin
%Y Maosong, Sun
%Y Jiye, Liang
%Y Xianpei, Han
%Y Zhiyuan, Liu
%Y Yulan, He
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G zho
%F shuaishuai-etal-2024-ji
%X “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。”
%U https://aclanthology.org/2024.ccl-1.36/
%P 467-480
Markdown (Informal)
[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)](https://aclanthology.org/2024.ccl-1.36/) (Zhou et al., CCL 2024)
ACL