Figure 6: Training precision by training epoch for selected datasets (mean over 10 training folds) tions of all predictions made. We report mean and standard deviation of the training precision, as well as the mean and standard deviation of the precision achieved on the validation fold, across all 10 folds. We train the RNN for 100 epochs on each training dataset. Fig. 6 plots training and validation precision for each epoch for a selection of our datasets (averaged across all 10 training folds). The plot shows that 100 training epochs are sufficient for optimal and stable results. The small BPI 2012 A and O datasets converge quickly to a high precision, whereas this occurs more gradually for the BPI 2013 datasets. Moreover, the BPI 2012 A, BPI 2012 O, and BPI 2013 Problem datasets with relatively small vocabularies converge faster than the BPI 2012 W and BPI 2013 Incident datasets that include resource or organizational group information and consequently have a larger vocabulary. There is a significant improvement in precision when the training rate is adjusted in epoch 50, suggesting that a dynamic 17 6. Experimental Results Training Precision by Epoch Epoch Tr aining Precision 0.2 0.4 0.6 0.8 0 20 40 60 80 100 BPIC 2012 A BPIC 2012 O BPIC 2012 W Completion Events (Resource) BPIC 2013 Incidents (Group) BPIC 2013 Problems Validation Precision by Epoch Epoch V alidation Precision 0.2 0.4 0.6 0.8 0 20 40 60 80 100