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In recent years, distantly-supervised relation extraction has achieved acertain success by using deep neural networks. Distant Supervision (DS) canautomatically generate large-scale annotated data by aligning entity pairs fromKnowledge Bases (KB) to sentences. However, these DS-generated datasetsinevitably have wrong labels that result in incorrect evaluation scores duringtesting, which may mislead the researchers. To solve this problem, we build anew dataset NYTH, where we use the DS-generated data as training data and hireannotators to label test data. Compared with the previous datasets, NYT-H has amuch larger test set and then we can perform more accurate and consistentevaluation. Finally, we present the experimental results of several widely usedsystems on NYT-H. The experimental results show that the ranking lists of thecomparison systems on the DS-labelled test data and human-annotated test dataare different. This indicates that our human-annotated data is necessary forevaluation of distantly-supervised relation extraction.

Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction