  Distant supervision can effectively label data for relation extraction, but
suffers from the noise labeling problem. Recent works mainly perform soft
bag-level noise reduction strategies to find the relatively better samples in a
sentence bag, which is suboptimal compared with making a hard decision of false
positive samples in sentence level. In this paper, we introduce an adversarial
learning framework, which we named DSGAN, to learn a sentence-level
true-positive generator. Inspired by Generative Adversarial Networks, we regard
the positive samples generated by the generator as the negative samples to
train the discriminator. The optimal generator is obtained until the
discrimination ability of the discriminator has the greatest decline. We adopt
the generator to filter distant supervision training dataset and redistribute
the false positive instances into the negative set, in which way to provide a
cleaned dataset for relation classification. The experimental results show that
the proposed strategy significantly improves the performance of distant
supervision relation extraction comparing to state-of-the-art systems.
