HAIN: Hierarchical Aggregation and Inference Network for Document-Level Relation Extraction

Document-level relation extraction (RE) aims to extract relations between entities within a document. Unlike sentence-level RE, it requires integrating evidences across multiple sentences. However, current models still lack the ability to effectively obtain relevant evidences for relation inference from multi-granularity information in the document. In this paper, we propose Hierarchical Aggregation and Inference Network (HAIN), performing the model to effectively predict relations by using global and local information from the document. Specifically, HAIN first constructs a meta dependency graph (mDG) to capture rich long distance global dependency information across the document. It also constructs a mention interaction graph (MG) to model complex local interactions among different mentions. Finally, it creates an entity inference graph (EG), based on which we design a novel hybrid attention mechanism to integrate relevant global and local information for entities. Experimental results demonstrate that our model achieves superior performance on a large-scale document-level dataset (DocRED). Extensive analyses also show that the model is particularly effective in extracting relations between entities across multiple sentences and mentions.