通过粒度球知识表示的联合开放意图分类
Jie Zhang1, Yanhua Li1, Xiaocao Ouyang1
1School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, 611130, Sichuan, China.
概括
联邦开放意图分类 (OIC) 通过实现分散的意图识别来解决隐私问题. 我们的颗粒球方法提高了准确性,并在开放世界的场景中保持了结构.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 自然语言处理自然语言处理.
背景情况:
- 开放意图分类 (OIC) 识别了开放世界的设置中已知的和未知的用户意图.
- 现有的OIC方法需要集中数据,这给隐私带来了挑战.
- 联合学习提供了去中心化的方法,但面临着诸如类不一致性和不受控制的伪未知样本生成等问题.
研究的目的:
- 为开放意图分类提出一个保护隐私的联合学习方法.
- 解决类不一致性,客户端特定分布和伪未知样本生成在联合OIC中的挑战.
- 为了使已知意图识别和未知意图检测在分散的设置中.
主要方法:
- 通过颗粒球表示形式 (FedOC-GB) 进行联邦开放意图分类.
- 客户构建颗粒球表示,以捕捉本地类结构并生成结构意识的伪未知样本.
- 服务器聚合了模型参数和细粒度的知识,形成了一个统一的全球模型和知识库.
主要成果:
- 在基准数据集上,FedOC-GB的表现始终优于最先进的联合OIC方法.
- 平均达到高达4.3%的F1-All和3.7%的ACC.
- 证明了颗粒状球代表对于关注隐私和保护结构的联合OIC的有效性.
结论:
- 联邦公开意图识别系统 (FedOC-GB) 成功实现了对隐私意识和结构保护的联合开放意图识别.
- 颗粒球的代表性是克服分散的OIC挑战的关键.
- 拟议的方法为具有隐私限制的现实世界OIC应用程序提供了强大的解决方案.
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