Acquiring Common Sense Spatial Knowledge through Implicit Spatial Templates
Table 2: Results on generalized triplets (top) and generalized words (bottom) (see Sect. 4.2). The tables on the right show results on the same sets without imposing generalization conditions, i.e., allowing to see all combinations/words during training.
['[EMPTY]', '[EMPTY]', 'Generalization R2', 'Generalization acc [ITALIC] y', 'Generalization F1 [ITALIC] y', 'Generalization r [ITALIC] x', 'Generalization r [ITALIC] y', 'Generalization IoU', 'Generalization px-acc', 'No Generalization R2', 'No Generalization acc [ITALIC] y', 'No Generalization F1 [ITALIC] y', 'No Generalization r [ITALIC] x', 'No Generalization r [ITALIC] y', 'No Generalization IoU', 'No Generalization px-acc']
Again, 1H performs slightly better, yet only significantly better than RND (p < 0.005). Notably, the good performance of RND on generalized triplets evidences that the model does not rely on external knowledge (word embeddings) to predict unseen combinations. This ability of generalizing from frequent combinations ( S, R, O) to rare/unseen ones is especially valuable given the sparsity of implicit combinations and the impossibility of learning all of them from data. Contrarily, larger performance differences are observed with generalized words (Tab. where, as expected, EMB outperforms RND and 1H embeddings by a margin thanks to the transference of knowledge from word embeddings combined with the acquired spatial knowledge.