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What do you think is the single most influential factor in determining with whom you become friends and whom you form romantic relationships? You might be surprised to learn that the answer is simple: the people with whom you have the most contact. This most important factor is proximity. You are more likely to be friends with people you have regular contact with. For example, there are decades of research that shows that you are more likely to become friends with people who live in your dorm,...
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    本研究介绍了一种使用预训练语言模型 (PLM) 和模板规范化进行少数拍摄关系提取 (FSRE) 的新方法. 拟议的模板规范化网络 (TRN) 通过减轻模板偏差来稳定FSRE模型,在基准数据集上实现卓越的性能.

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    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 (RE) 面临的挑战是有限的监督培训数据.
    • 收集高质量的,广泛的RE数据集是困难的和资源密集的.
    • 预训练语言模型 (PLM) 提供了知识提取的潜力,但需要有效的提示策略.

    研究的目的:

    • 使用PLM开发一种可靠的几次射击关系提取 (FSRE) 方法.
    • 为解决FSRE提示技术中的模板偏差问题.
    • 在低数据场景中提高FSRE模型的稳定性和性能.

    主要方法:

    • 通过提示技术从PLM中获得关系和事实知识.
    • 自动生成FSRE的各种自然语言模板.
    • 提出一个模板规范化网络 (TRN) 以减轻模板偏差.
    • 实施TRN与注意力,排名规范化和模板校准机制.

    主要成果:

    • 拟议的TRN有效地减轻模板偏差,稳定FSRE模型.
    • 在FewRel和NYT数据集上的实验结果表明,它们在现有方法上具有强大的优势.
    • 该模型显示,在几次拍摄的关系提取任务中,有显著的改进.

    结论:

    • 开发的提示和规范化方法为少数拍摄关系提取提供了稳定有效的解决方案.
    • 这种方法成功地利用PLM在低资源环境中获取知识.
    • 这些发现表明,在有限的数据基础上,推进可再生能源能力是一个有希望的方向.