Notably, we observe that when training on data with large labeling noises, different parameter initializations can heavily influence the extraction performance of trained models.
as mentioned in the main body, to clearly and fairly show the actual impact of different types of training labels, we restart the training of NRE models with 5 random seeds, ranging from 0 to 4, for each case and report the averaged scores.