  Deep residual learning (ResNet) is a new method for training very deep neural
networks using identity map-ping for shortcut connections. ResNet has won the
ImageNet ILSVRC 2015 classification task, and achieved state-of-the-art
performances in many computer vision tasks. However, the effect of residual
learning on noisy natural language processing tasks is still not well
understood. In this paper, we design a novel convolutional neural network (CNN)
with residual learning, and investigate its impacts on the task of distantly
supervised noisy relation extraction. In contradictory to popular beliefs that
ResNet only works well for very deep networks, we found that even with 9 layers
of CNNs, using identity mapping could significantly improve the performance for
distantly-supervised relation extraction.
