Reasoning with Latent Structure Refinement for Document-Level Relation Extraction
Overview
This paper introduces a novel approach for document-level relation extraction, which is a crucial task in natural language processing, requiring the synthesis of information spread across multiple sentences in a document to identify relationships between entities. Traditional models for relation extraction typically focus on intra-sentence relations, but the need to understand broader contexts in domains such as biomedical research has spurred interest in document-level methods. The authors propose a model that automatically induces a latent document-level graph to facilitate relational reasoning across sentences. This model does not rely on syntactic trees or predefined co-reference chains to construct the document-level structure but instead employs a dynamic framework that iteratively refines its understanding of inter-entity dependencies.
Model Components and Methodology
The proposed Latent Structure Refinement (LSR) model consists of three main components:
- Node Constructor: This component utilizes a context encoder (such as BiLSTM or BERT) to generate contextualized representations of document sentences. It extracts mention nodes, entity nodes, and meta dependency paths (MDP) nodes to form the basis for the graph structure.
- Dynamic Reasoner: Central to the model's novelty, the dynamic reasoner comprises two submodules: structure induction and multi-hop reasoning. The structure induction module leverages structured attention to induce a task-specific latent graph structure without reliance on external syntactic features. The reasoner applies graph convolutional networks (GCNs) to refine node representations iteratively. It refines these latent structures over multiple iterations to capture complex and non-local interactions effectively.
- Classifier: Using the refined node representations, the classifier predicts relations between entity pairs through a bilinear function.
Experimental Results
The LSR model demonstrates significant improvements over existing approaches on the DocRED dataset, achieving a new state-of-the-art with an F1 score of 59.05 when incorporating BERT as a context encoder. This advancement underscores the model's capability to address both intra- and inter-sentence relations better than prior models. The approach also outperforms other graph-based methods that construct static graphs based on syntactic or heuristic rules, highlighting the benefits of a dynamic, latent structure.
In addition to performing robustly on the generic DocRED dataset, the paper's approach also achieved promising results in the biomedical domain datasets, such as CDR and GDA, which further speaks to the model's adaptability when parsing domain-specific texts.
Implications and Future Directions
This research presents multiple potential impacts on both theoretical and practical fronts. Theoretically, the iterative refinement strategy of the latent graph offers a principled way to frame and solve document-level relation extraction as a dynamic learning problem, paving the way for future work in adaptive graph reasoning. Practically, the ability of LSR to improve information synthesis across document structures can significantly advance natural language understanding applications in various sectors, especially fields where contextual accuracy and depth of information extraction are paramount, such as legal document analysis or biomedical research.
Future explorations could focus on further optimizing the graph construction process to handle specific domain requirements without dependency on external parsers, refining the steps of iterative improvements, or integrating more sophisticated interaction models tailored to specific relation categories. These directions can potentially benefit from advances in knowledge representation and deep learning methodologies that align with the latent structure modeling approach.