Improving Graph Convolutional Networks Based on Relation-Aware Attention for End-to-End Relation Extraction

In this paper, we present a novel end-to-end neural model based on graph convolutional networks (GCN) for jointly extracting entities and relations between them. It divides the joint extraction into two sub-tasks, first detecting entity spans and identifying entity relations type simultaneously. To consider the complete interaction between entities and relations, we propose a novel relation-aware attention mechanism to obtain the relation representation between two entity spans. Therefore, a complete graph is constructed based on all extracted entity spans where the nodes are entity spans and the edges are relation representation. Besides, we improve original GCN to utilize both adjacent node features and edge information when encoding node feature. Experiments are conducted on two public datasets and our model outperforms all baseline methods.