快速对比学习用于零射击关系提取
Xueyi Zhong1, Liye Zhao2, Licheng Peng2
1School of Finance, Southwestern University of Finance and Economics, Chengdu 611130, China.
Entropy (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了用于关系提取 (PCRE) 的快速对比学习,通过利用预先训练的语言模型来改进零射击学习. PCRE增强了语义表示,以便更好地提取看不见的关系.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 关系提取对于知识获取至关重要,但需要大量的数据注释.
- 零射击学习解决了注释成本,使模型能够识别看不见的关系.
- 目前的零射击方法与多样化的任务配方扎,导致性能不佳.
研究的目的:
- 通过利用预先训练的语言模型的知识,开发一种新的零射击关系提取方法.
- 改进关系提取模型的语义表示能力,用于看不见的关系.
- 引入一个Prompt-Contrastive学习框架 (PCRE) 进行增强的零射击关系提取.
主要方法:
- 通过即时调整,利用预先训练的语言模型的语义知识.
- 用各种提示模板来增强实例,以创建对比学习的双视图.
- 实现一个实例描述对比的目标,从文本描述中提取关系知识.
主要成果:
- 拟议的PCRE方法在零射击关系提取方面显著优于现有的最先进的基线.
- 实验结果表明PCRE在各种数据集和训练配置中的稳定性.
- PCRE有效地提高了模型区分可见和不可见关系的能力.
结论:
- 通过有效利用预先训练的语言模型,PCRE为改善零射击关系提取提供了一个有希望的方向.
- 快速对比的学习策略增强了语义表示,从而在识别新关系方面取得了更好的表现.
- 该方法的稳定性表明它在现实世界知识提取任务中具有广泛的适用性.
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