  Recent efforts for information extraction have relied on many deep neural
models. However, any such models can easily overfit noisy labels and suffer
from performance degradation. While it is very costly to filter noisy labels in
large learning resources, recent studies show that such labels take more
training steps to be memorized and are more frequently forgotten than clean
labels, therefore are identifiable in training. Motivated by such properties,
we propose a simple co-regularization framework for entity-centric information
extraction, which consists of several neural models with different parameter
initialization. These models are jointly optimized with task-specific loss, and
are regularized to generate similar predictions based on an agreement loss,
which prevents overfitting on noisy labels. In the end, we can take any of the
trained models for inference. Extensive experiments on two widely used but
noisy benchmarks for information extraction, TACRED and CoNLL03, demonstrate
the effectiveness of our framework.
