An Adversarial Training Framework for Relation Classification

Wenpeng Liu, Yanan Cao, Cong Cao, Yanbing Liu, Yue Hu, Li Guo

Abstract: Relation classification is one of the most important topics in Natural Language Processing (NLP) which could help mining structured facts from text and constructing knowledge graph. Although deep neural network models have achieved improved performance in this task, the state-of-the-art methods still suffer from the scarce training data and the overfitting problem. In order to solve this problem, we adopt the adversarial training framework to improve the robustness and generalization of the relation classifier. In this paper, we construct a bidirectional recurrent neural network as the relation classifier, and append word-level attention to the input sentence. Our model is an end-to-end framework without the use of any features derived from pre-trained NLP tools. In experiments, our model achieved higher F1-score and better robustness than comparative methods.