Discriminative Reasoning for Document-level Relation Extraction
This addresses the problem of improving relation extraction accuracy for natural language processing applications, representing an incremental advancement over existing graph-based methods.
The paper tackles document-level relation extraction by proposing a discriminative reasoning framework that explicitly models reasoning paths between entity pairs, achieving state-of-the-art performance on a large-scale dataset.
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.