OhioState at SemEval-2018 Task 7: Exploiting Data Augmentation for Relation Classification in Scientific Papers Using Piecewise Convolutional Neural Networks
Table 2: Results of our best performing systems on the official test set with/without data augmentation.
['[BOLD] Task', '[BOLD] Data', '[BOLD] Epoch', '[BOLD] Batch Size', '[BOLD] No. of Filters', '[BOLD] Macro-F1 Score']
We report our performance on the classification tasks (Subtask 1.1 and 1.2) according to the official evaluation. Even a simple mixing of the two datasets which differ significantly in the nature of tagged entities lead to a significant improvement. Surprisingly though, adding the noisy data to the clean dataset also leads to a 36% increase in performance. This could be attributed to the fact that while heuristically annotated entities are high-level concepts thus sharing a lot of context with similar concepts, most of the manually annotated entities are full noun phrases, thus adding to the complexity of the task. These results also falsify our initial assumption/expectation of Task 1.1 to be easier.