论文标题
SPE：对称提示提高事实探测
SPE: Symmetrical Prompt Enhancement for Fact Probing
论文作者
论文摘要
预验证的语言模型（PLM）已被证明可以在预处理过程中积累事实知识（Petroni等，2019）。最近的作品通过离散或连续形式的提示来探讨这些知识的范围。但是，这些方法不考虑任务的对称性：对象预测和主题预测。在这项工作中，我们提出了对称提示增强（SPE），这是一种基于持续的及时及时探测的方法，用于在PLM中进行事实探测，该方法通过构造对象和对象预测来利用任务的对称性来利用任务的对称性。我们对流行的事实探测数据集LAMA的结果表现出对先前探测方法的显着改善。
Pretrained language models (PLMs) have been shown to accumulate factual knowledge during pretrainingng (Petroni et al., 2019). Recent works probe PLMs for the extent of this knowledge through prompts either in discrete or continuous forms. However, these methods do not consider symmetry of the task: object prediction and subject prediction. In this work, we propose Symmetrical Prompt Enhancement (SPE), a continuous prompt-based method for factual probing in PLMs that leverages the symmetry of the task by constructing symmetrical prompts for subject and object prediction. Our results on a popular factual probing dataset, LAMA, show significant improvement of SPE over previous probing methods.
