Improving Cross-Domain Performance for Relation Extraction via Dependency Prediction and Information Flow Control
Table 6: Complexity analysis of the models. The first columns show how much of training data has been used for training. Performance is on the ACE 2005 development set.
['[EMPTY]', '[BOLD] CNN', '[BOLD] RNN', '[BOLD] C-GCN', '[BOLD] LISA', '[BOLD] DRPC']
In this section, we first evaluate the sample complexity of the models to better understand their operation. In particular, we choose different subsets of the ACE 2005 training dataset according to different ratios of the size (i.e., 10%, 20%, 30% etc.). Such subsets are then used to train the models to evaluate their performance. As we can see from the table, DRPC is significantly better than all the baselines for different amounts of training data, thus demonstrating the better sample complexity of the proposed model for RE.