Learning from Noisy Labels for Entity-Centric Information Extraction
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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.
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