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Distant supervision (DS) is an important paradigm for automatically extracting relations. It utilizes existing knowledge base to collect examples for the relation we intend to extract, and then uses these examples to automatically generate the training data. However, the examples collected can be very noisy, and pose significant challenge for obtaining high quality labels. Previous work has made remarkable progress in predicting the relation from distant supervision, but typically ignores thedoi:10.18653/v1/n19-1107 dblp:conf/naacl/YanHHLL19 fatcat:nldkpb5purfupo46rsjbxp7a2a