Learning Relational Dependency Networks for Relation Extraction
Table 6: Joint Learning Results comparing models trained individually (IL) and models trained with jointly for all relations (JL).
['Relation', 'AUC ROC IL', 'AUC ROC JL']
Recall and F1 are omitted for conciseness – the conclusions are the same across all metrics. Joint learning appears to help in about half of the relations (8/14). Particularly, in person category, joint learning with gold standard outperforms their individual learning counterparts. This is due to the fact that some relations such as parents, spouse, siblings etc. are inter-related and learning them jointly indeed improves performance. Hence Q2 can be answered affirmatively for half the relations.