论文标题
在远处监督下，与加权对比预训练的关系提取
Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision
论文作者
论文摘要
对远处监督的对比预训练在改善监督关系提取任务方面表现出了显着的有效性。但是，现有方法忽略了在训练阶段的远处监督的固有噪声。在本文中，我们通过利用监督数据来估计预训练实例的可靠性并明确降低噪声的效果来提出一种加权对比度学习方法。与两个最新的非加权基线相比，三个监督数据集的实验结果证明了我们提出的加权对比度学习方法的优势。我们的代码和模型可在以下位置提供：https：//github.com/yukinowan/yukinowan/wcl
Contrastive pre-training on distant supervision has shown remarkable effectiveness in improving supervised relation extraction tasks. However, the existing methods ignore the intrinsic noise of distant supervision during the pre-training stage. In this paper, we propose a weighted contrastive learning method by leveraging the supervised data to estimate the reliability of pre-training instances and explicitly reduce the effect of noise. Experimental results on three supervised datasets demonstrate the advantages of our proposed weighted contrastive learning approach compared to two state-of-the-art non-weighted baselines.Our code and models are available at: https://github.com/YukinoWan/WCL
