  Distant supervision for relation extraction enables one to effectively
acquire structured relations out of very large text corpora with less human
efforts. Nevertheless, most of the prior-art models for such tasks assume that
the given text can be noisy, but their corresponding labels are clean. Such
unrealistic assumption is contradictory with the fact that the given labels are
often noisy as well, thus leading to significant performance degradation of
those models on real-world data. To cope with this challenge, we propose a
novel label-denoising framework that combines neural network with probabilistic
modelling, which naturally takes into account the noisy labels during learning.
We empirically demonstrate that our approach significantly improves the current
art in uncovering the ground-truth relation labels.
