Adaptive Graph Convolutional Networks with Attention Mechanism for Relation Extraction

Abstract

In the relationship extraction task of NLP, how to effective use of the rich structural information on the dependency tree is a challenging research problem. 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. Unlike using an attention mechanism to effectively make use of relevant information, we propose a Dual Attention Graph Convolutional Network (DAGCN) to adaptively integrate local features with their global dependencies. Specifically, we append two types of attention modules on top of GCN, which model the semantic interdependencies in spatial and relational dimensions respectively. The position attention module selectively aggregates the feature at each position by a weighted sum of the features at all positions of nodes internal features. Similar features would be related to each other regardless of their distances. Meanwhile, the relation attention module selectively emphasizes interdependent node relations by integrating associated features among all 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 TACRED and SemEval datasets show that the model can obtain more useful information for relational extraction tasks, and achieve better performances on various evaluation indexes.