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Relation extraction models suffer from limited qualified training data. Usinghuman annotators to label sentences is too expensive and does not scale wellespecially when dealing with large datasets. In this paper, we use AuxiliaryClassifier Generative Adversarial Networks (AC-GANs) to generate high-qualityrelational sentences and to improve the performance of relation classifier inend-to-end models. In AC-GAN, the discriminator gives not only a probabilitydistribution over the real source, but also a probability distribution over therelation labels. This helps to generate meaningful relational sentences.Experimental results show that our proposed data augmentation methodsignificantly improves the performance of relation extraction compared tostate-of-the-art methods

An Auxiliary Classifier Generative Adversarial Framework for Relation Extraction