  Distant supervision has been widely used for relation extraction but suffers
from noise labeling problem. Neural network models are proposed to denoise with
attention mechanism but cannot eliminate noisy data due to its non-zero
weights. Hard decision is proposed to remove wrongly-labeled instances from the
positive set though causes loss of useful information contained in removed
instances. In this paper, we propose a novel generative neural framework named
RDSGAN (Rank-based Distant Supervision GAN) which automatically generates valid
instances for distant supervision relation extraction. Our framework combines
soft attention and hard decision to learn the distribution of true positive
instances via adversarial training and selects valid instances conforming to
the distribution via rank-based distant supervision, which addresses the false
positive problem. Experimental results show the superiority of our framework
over strong baselines.
