An Auxiliary Classifier Generative Adversarial Framework for Relation Extraction

Relation extraction models suffer from limited qualified training data. Using human annotators to label sentences is too expensive and does not scale well especially when dealing with large datasets. In this paper, we use Auxiliary Classifier Generative Adversarial Networks (AC-GANs) to generate high-quality relational sentences and to improve the performance of relation classifier in end-to-end models. In AC-GAN, the discriminator gives not only a probability distribution over the real source, but also a probability distribution over the relation labels. This helps to generate meaningful relational sentences.