增强知识的原型网络与类集群损失为少数射击关系分类的分类
Tao Liu1,2,3, Zunwang Ke1,2,3, Yanbing Li2,3
1College of Software, Xinjiang University, Urumqi, China.
PloS one
|June 8, 2023
概括
这项研究引入了一种新的方法,用于几次射击关系分类,改善模型概括和处理类似样本. 该方法增强了原型表示,并使用新的类集群损失来更好地进行特征歧视.
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:
- 短暂关系分类 (FRC) 旨在识别实体之间的关系,使用有限的标记数据.
- 现有的原型网络方法通常包含外部知识,但由于复杂的网络结构,难以进行概括.
- 当前的模型往往忽视了类内紧性,阻碍了它们有效管理异常样本的能力.
研究的目的:
- 提出一种用于少数射击关系分类的新方法,可以克服模型概括和异常值处理的局限性.
- 在FRC模型中增强类原型的表示能力.
- 提高学习特征空间的区分能力.
主要方法:
- 引入了一个非加权的原型增强模块,利用特征级别的相似性来进行特征过和完成.
- 设计了一个类集群损失函数,该函数明确强制执行类内紧性和类间可分离性.
- 采用技术来采样艰难的正面和负面实例,以进行强有力的培训.
主要成果:
- 拟议的模型在FewRel 1.0和2.0数据集上显示出显著的有效性.
- 非加权的原型增强模块改善了原型的表现.
- 类集群损失增强了模型学习高度歧视性度量空间的能力.
结论:
- 开发的方法为少数射击关系分类提供了更有效的解决方案.
- 提出的方法有助于在FRC模型中更好地概括和处理异常值.
- 这些发现强调了明确的课内和课外约束在FRC的度量学习中的重要性.
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