An Interpretable Knowledge Transfer Model for Knowledge Base Completion
Table 3: Performance of model with dense attention vectors or sparse attention vectors. MR, H10 and Time denotes mean rank, Hits@10 and training time per epoch respectively
['[BOLD] Method', '[BOLD] WN18 MR', '[BOLD] WN18 H10', '[BOLD] WN18 Time', '[BOLD] FB15k MR', '[BOLD] FB15k H10', '[BOLD] FB15k Time']
Generally, ITransF with sparse attention has slightly better or comparable performance comparing to dense attention. We see that ℓ1 regularization does not produce a sparse attention, especially on FB15k.