Document-level relation extraction (DocRE) aims to infer complex semantic relations among entities in a document. Distant supervision (DS) is able to generate massive auto-labeled data, which can improve DocRE performance. Recent works leverage pseudo labels generated by the pre-denoising model to reduce noise in DS data. However, unreliable pseudo labels bring new noise, e.g., adding false pseudo labels and losing correct DS labels. Therefore, how to select effective pseudo labels to denoise DS data is still a challenge in document-level distant relation extraction. To tackle this issue, we introduce uncertainty estimation technology to determine whether pseudo labels can be trusted. In this work, we propose a Document-level distant Relation Extraction framework with Uncertainty Guided label denoising, UGDRE. Specifically, we propose a novel instance-level uncertainty estimation method, which measures the reliability of the pseudo labels with overlapping relations. By further considering the long-tail problem, we design dynamic uncertainty thresholds for different types of relations to filter high-uncertainty pseudo labels. We conduct experiments on two public datasets. Our framework outperforms strong baselines by 1.91 F1 and 2.28 Ign F1 on the RE-DocRED dataset.

翻译：文档级关系抽取（DocRE）旨在推断文档中实体间的复杂语义关系。远程监督（DS）能够生成大量自动标注数据，从而提升DocRE性能。近期研究利用预去噪模型生成的伪标签来减少DS数据中的噪声。然而，不可靠的伪标签会引入新噪声，例如添加虚假伪标签或丢失正确的DS标签。因此，如何选择有效的伪标签以对DS数据进行去噪，仍是文档级远距离关系抽取中的一项挑战。为解决此问题，我们引入不确定性估计技术以判断伪标签是否可信。本文提出一种不确定性引导标签去噪的文档级远距离关系抽取框架UGDRE。具体而言，我们提出一种新颖的实例级不确定性估计方法，用于衡量具有重叠关系的伪标签的可靠性。通过进一步考虑长尾问题，我们针对不同关系类型设计动态不确定性阈值，以过滤高不确定性伪标签。我们在两个公开数据集上进行实验。在RE-DocRED数据集上，我们的框架以1.91 F1和2.28 Ign F1的性能超越强基线方法。