Reasoning with Latent Structure Refinement for Document-Level Relation Extraction

TL;DR

This paper introduces a novel latent graph-based model for document-level relation extraction that automatically induces and refines relational structures, significantly improving accuracy on multiple datasets.

Contribution

It proposes a new model with latent structure induction and refinement strategies for better multi-hop reasoning in document-level relation extraction.

Findings

Achieves 59.05 F1 on DocRED, surpassing previous methods.

Sets new state-of-the-art on CDR and GDA datasets.

Effectively discovers more accurate inter-sentence relations.

Abstract

Document-level relation extraction requires integrating information within and across multiple sentences of a document and capturing complex interactions between inter-sentence entities. However, effective aggregation of relevant information in the document remains a challenging research question. Existing approaches construct static document-level graphs based on syntactic trees, co-references or heuristics from the unstructured text to model the dependencies. Unlike previous methods that may not be able to capture rich non-local interactions for inference, we propose a novel model that empowers the relational reasoning across sentences by automatically inducing the latent document-level graph. We further develop a refinement strategy, which enables the model to incrementally aggregate relevant information for multi-hop reasoning. Specifically, our model achieves an F1 score of 59.05 on a large-scale document-level dataset (DocRED), significantly improving over the previous results, and also yields new state-of-the-art results on the CDR and GDA dataset. Furthermore, extensive analyses show that the model is able to discover more accurate inter-sentence relations.