Deconstructing reasoning paths and attending to semantic guidance for document-level relation extraction

Abstract

Extracting relations from a document is more challenging than from a sentence, due to the involvement of more entities and complex contextual information. To capture long-distance contextual dependencies, graph networks are widely used in document-level relation extraction. However, lengthy reasoning paths, built by most models based on graph networks to capture dependency, will result in the gradual accumulation of noise information as these paths extend. Furthermore, semantic information that is not explicitly present will be easily overlooked due to the utilization of heuristic rules for constructing document structural feature-based static graphs. To tackle these issues, we propose a novel framework to extract document-level relations by Deconstructing Reasoning Paths and Attending to Semantic Guidance, namely DRPASG. The model progressively deconstructs reasoning paths and dynamically aggregates multi-level representations of entities. Furthermore, a unique attention regularizer is constructed to guide the model in focusing on uncaptured semantic correlations, facilitating the transmission and interaction of both structural and semantic information within the document. Experimental results on the document-level dataset DocRED indicate that the proposed model outperforms previous state-of-the-art methods. Extensive analysis validates the effectiveness of our method in performing multi-hop reasoning.