  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.
