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Distant supervision based methods for entity and relation extraction havereceived increasing popularity due to the fact that these methods require lighthuman annotation efforts. In this paper, we consider the problem of\textit{shifted label distribution}, which is caused by the inconsistencybetween the noisy-labeled training set subject to external knowledge graph andthe human-annotated test set, and exacerbated by the pipelinedentity-then-relation extraction manner with noise propagation. We propose ajoint extraction approach to address this problem by re-labeling noisyinstances with a group of cooperative multiagents. To handle noisy instances ina fine-grained manner, each agent in the cooperative group evaluates theinstance by calculating a continuous confidence score from its own perspective;To leverage the correlations between these two extraction tasks, a confidenceconsensus module is designed to gather the wisdom of all agents andre-distribute the noisy training set with confidence-scored labels. Further,the confidences are used to adjust the training losses of extractors.Experimental results on two real-world datasets verify the benefits ofre-labeling noisy instance, and show that the proposed model significantlyoutperforms the state-of-the-art entity and relation extraction methods.

Relabel the Noise: Joint Extraction of Entities and Relations via Cooperative Multiagents