OhioState at SemEval-2018 Task 7: Exploiting Data Augmentation for Relation Classification in Scientific Papers Using Piecewise Convolutional Neural Networks
Table 1: Hyperparameter Values.
['[BOLD] Parameter', '[BOLD] Values']
While the final training and prediction was performed on the entire training dataset, we use the official validation split provided by contest organizers to perform hyper-parameter tuning. For the data augmentation scenario, however, we also make use of the validation data from the other task. For the remaining parameters, we used standard values as recommended by prior literature as follows: convolution filters of width 3,4 and 5; position and directionality embeddings of size 5; windows size for relative positions from entities was set to 30.