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通过相互定的对比学习利用实例-标签动态来进行少数拍摄关系提取.

Yanglei Gan1, Qiao Liu1, Run Lin1

  • 1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China.

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概括

这项研究引入了相互定对比学习 (RACL) 用于几次拍摄的关系提取,改进了使用实例和标签信息的方式. RACL通过统一实例和标签视角来增强语义表示,以便从有限的数据中更好地提取关系.

关键词:
相反的学习学习.几次拍摄的关系提取.精细调整了我们的声音.提取信息 提取信息在训练前的训练.

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科学领域:

  • 自然语言处理自然语言处理.
  • 机器学习 机器学习
  • 人工智能的人工智能

背景情况:

  • 短暂的关系提取 (FSRE) 旨在从最小的标记数据中识别关系.
  • 经过监督对比学习的预训练语言模型 (PLM) 通过使用实例和标签动态来推进FSRE.
  • 现有的方法在丰富的语义表示中未充分利用广泛的实例-标签对.

研究的目的:

  • 提出一种新的框架,即相互定对比学习 (RACL),用于短暂的关系提取.
  • 为了增强实例标签对的使用,在FSRE中实现更有语义丰富的表示.
  • 通过整合来自实例和标签信息的独特见解,创建一个统一的表示空间.

主要方法:

  • 为FSRE引入了相互定对比学习 (RACL) 框架.
  • 采用对称的对比目标,具有实例级和标签级的对比损失.
  • 专注于描述实例属性和关系事实之间的关系,同时优化信息共享.

主要成果:

  • 在FSRE基准数据集上证明了RACL在最先进的基线上的优越性.
  • 通过创建一个更整合和统一的代表空间,实现了更好的性能.
  • 通过在零射击和没有上述设置中的废除研究,验证了RACL的稳定性和适应性.

结论:

  • RACL有效地利用实例标签对的互补见解来改进FSRE.
  • 拟议的对称对比方法增强了语义理解和表示.
  • RACL在几次拍摄关系提取中的实际应用方面显示出显著的前景.