Compositional Vector Space Models for Knowledge Base Completion
Table 4: Results comparing the zero-shot model with supervised RNN and a random baseline on 10 types. RNN is the fully supervised model described in section 3 while zero-shot is the model described in section 4. The zero-shot model without explicitly training for the target relation types achieves impressive results by performing significantly (p<0.05) better than a random baseline.
['[EMPTY]', 'train with top 1000 paths', 'train with all paths']
We evaluate on randomly selected 10 (out of 46) relation types, hence for the fully supervised version we train 10 RNNs, one for each relation type. For evaluating the zero-shot model, we randomly split the relations into two sets of equal size and train a zero-shot model on one set and test on the other set. So, in this case we have two RNNs making predictions on relation types that they have never seen during training. As expected, the fully supervised RNN outperforms the zero-shot model by a large margin but the zero-shot model without using any direct supervision clearly performs much better than a random baseline.