  Entity resolution targets at identifying records that represent the same
real-world entity from one or more datasets. A major challenge in
learning-based entity resolution is how to reduce the label cost for training.
Due to the quadratic nature of record pair comparison, labeling is a costly
task that often requires a significant effort from human experts. Inspired by
recent advances of generative adversarial network (GAN), we propose a novel
deep learning method, called ErGAN, to address the challenge. ErGAN consists of
two key components: a label generator and a discriminator which are optimized
alternatively through adversarial learning. To alleviate the issues of
overfitting and highly imbalanced distribution, we design two novel modules for
diversity and propagation, which can greatly improve the model generalization
power. We have conducted extensive experiments to empirically verify the
labeling and learning efficiency of ErGAN. The experimental results show that
ErGAN beats the state-of-the-art baselines, including unsupervised,
semi-supervised, and unsupervised learning methods.
