  Either human annotation or rule based automatic labeling is an effective
method to augment data for relation extraction. However, the inevitable wrong
labeling problem for example by distant supervision may deteriorate the
performance of many existing methods. To address this issue, we introduce a
practical end-to-end deep learning framework, including a standard feature
extractor and a novel noisy classifier with our proposed doubly transitional
mechanism. One transition is basically parameterized by a non-linear
transformation between hidden layers that implicitly represents the conversion
between the true and noisy labels, and it can be readily optimized together
with other model parameters. Another is an explicit probability transition
matrix that captures the direct conversion between labels but needs to be
derived from an EM algorithm. We conduct experiments on the NYT dataset and
SemEval 2018 Task 7. The empirical results show comparable or better
performance over state-of-the-art methods.
