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In recent years there is a surge of interest in applying distant supervision (DS) to automatically generate training data for relation extraction (RE). In this paper, we study the problem what limits the performance of DS-trained neural models, conduct thorough analyses, and identify a factor that can influence the performance greatly, shifted label distribution. Specifically, we found this problem commonly exists in real-world DS datasets, and without special handing, typical DS-RE modelsdoi:10.18653/v1/d19-1397 dblp:conf/emnlp/YeLZR19 fatcat:vyfqxgd5d5hlzd7dqlg4ud3ueq