Adversarial Training for Relation Extraction
Yi Wu, David Bamman, Stuart Russell · 2017
Adversarial training is a mean of regularizing classification algorithms by generating adversarial noise to the training data.We apply adversarial training in relation extraction within the multi-instance multi-label learning framework.We evaluate various neural network architectures on two different datasets.Experimental results demonstrate that adversarial training is generally effective for both CNN and RNN models and significantly improves the precision of predicted relations.