In general, the proposed approach is able to converge to a higher accuracy level much faster than either of the ablated models and the performance gap often persists until the end of training (on UMLS, Kinship, and FB15k-237).
on FB15k-237, our approach still shows improvement even after the two ablated models start to overfit, with −AD beginning to overfit sooner.