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We describe our system for SemEval-2018 Shared Task on Semantic RelationExtraction and Classification in Scientific Papers where we focus on theClassification task. Our simple piecewise convolution neural encoder performsdecently in an end to end manner. A simple inter-task data augmentationsignifi- cantly boosts the performance of the model. Our best-performingsystems stood 8th out of 20 teams on the classification task on noisy data and12th out of 28 teams on the classification task on clean data.

OhioState at SemEval-2018 Task 7: Exploiting Data Augmentation for  Relation Classification in Scientific Papers using Piecewise Convolutional  Neural Networks