Reinforcement Learning with Dual Attention Guided Graph Convolution for Relation Extraction

Abstract

To better learn the dependency relationship between nodes, we address the relationship extraction task by capturing rich contextual dependencies based on the attention mechanism, and using distributional reinforcement learning to generate optimal relation information representation. This method is called Dual Attention Graph Convolutional Network (DAGCN), to adaptively integrate local features with their global dependencies. Specifically, the samples are represented as nodes on the graph, and the relationships within and between nodes are studied. We consider the influence between node feature locations and associate each location information of the feature with other features. This allows the feature vector to contain a wider range of semantic information to enhance the ability of feature representation. We consider the information features of node dependence, use adjacent nodes to represent their own nodes, and encode the features of node relation, so as to enhance the global dependence between nodes. We sum the outputs of the two attention modules and use reinforcement learning to predict the classification of nodes relationship to further improve feature representation which contributes to more precise extraction results. The results on the common datasets show that the model can obtain more useful information for relational extraction tasks, and achieve better performances on various evaluation indexes.