Document-Level Relation Extraction with Deep Gated Graph Reasoning

Abstract

Extracting the relations of two entities on the sentence-level has drawn increasing attention in recent years but remains facing great challenges on document-level, due to the inherent difficulty in recognizing the relations of two entities across multiple sentences. Previous works show that employing the graph convolutional neural network can help the model capture unstructured dependent information of entities. However, they usually employed the non-adaptive weight edges to build the correlation weight matrix which suffered from the problem of information redundancy and gradient disappearance. To solve this problem, we propose a deep gated graph reasoning model for document-level relation extraction, namely, BERT-GGNNs, which employ an improved gated graph neural network with a learnable correlation weight matrix to establish multiple deep gated graph reason layers. The proposed deep gated graph reasoning layers make the model easier to reasoning the relations between entities hidden in the document. Experiments show that the proposed model outperforms most of strong baseline models, and our proposed model is 0.3% and 0.3% higher than the famous LSR-BERT model on the F1 and Ing F1, respectively.