Document-level relation extraction (DocRE) models generally use graph networks to implicitly model the reasoning skill (i.e., pattern recognition, logical reasoning, coreference reasoning, etc.) related to the relation between one entity pair in a document. In this paper, we propose a novel discriminative reasoning framework to explicitly model the paths of these reasoning skills between each entity pair in this document. Thus, a discriminative reasoning network is designed to estimate the relation probability distribution of different reasoning paths based on the constructed graph and vectorized document contexts for each entity pair, thereby recognizing their relation. Experimental results show that our method outperforms the previous state-of-the-art performance on the large-scale DocRE dataset. The code is publicly available at https://github.com/xwjim/DRN.

翻译：文件级关系提取(DocRE)模型通常使用图表网络来隐含地模拟与一份文件中的一对实体之间的关系有关的推理技巧(即模式识别、逻辑推理、共同参照推理等),在本文件中,我们提出一个新的歧视性推理框架,以明确模拟本文件中每一对实体之间这些推理技巧的路径,因此,一个歧视性推理网络旨在根据每个对实体的图解和矢量化文档背景来估计不同推理路径的概率分布,从而承认它们之间的关系。实验结果表明,我们的方法超过了以前在大型DocRE数据集上最先进的性能。该代码可在https://github.com/xwjim/DRN上公开查阅。