A Multi-Task Learning Framework for Extracting Drugs and Their Interactions from Drug Labels
(a) System performance on Official Test Set 1
['[BOLD] Training Data', '[BOLD] Method', 'Entity (Primary)  [BOLD] P (%)', 'Entity (Primary)  [BOLD] R (%)', 'Entity (Primary)  [BOLD] F (%)', 'Relation (Relaxed)  [BOLD] P (%)', 'Relation (Relaxed)  [BOLD] R (%)', 'Relation (Relaxed)  [BOLD] F (%)', 'Relation (Primary)  [BOLD] P (%)', 'Relation (Primary)  [BOLD] R (%)', 'Relation (Primary)  [BOLD] F (%)']
Eight teams participated in task 1 while four teams participated in task 2. For each team, we only display the performance of the best run for a particular test set. Methods are grouped by the data used for training and ranked in ascending order of primary relation extraction performance followed by entity recognition performance. We also included a single model trained solely on Training-22, that was not submitted, for comparison. Our voting-based ensemble performed best among the three systems submitted by our team on both NER and relation extraction. In the official challenge, this model placed second overall on both NER and relation extraction.