Zero-Shot Learning with Common Sense Knowledge Graphs

Abstract

Zero-shot learning relies on semantic class representations such as hand-engineered attributes or learned embeddings to predict classes without any labeled examples. We propose to learn class representations from common sense knowledge graphs. Common sense knowledge graphs are an untapped source of explicit high-level knowledge that requires little human effort to apply to a range of tasks. To capture the knowledge in the graph, we introduce ZSL-KG, a general-purpose framework with a novel transformer graph convolutional network (TrGCN) for generating class representations. Our proposed TrGCN architecture computes non-linear combinations of the node neighbourhood and shows improvements on zero-shot learning tasks in language and vision. Our results show ZSL-KG outperforms the best performing graph-based zero-shot learning framework by an average of 2.1 accuracy points with improvements as high as 3.4 accuracy points. Our ablation study on ZSL-KG with alternate graph neural networks shows that our TrGCN adds up to 1.2 accuracy points improvement on these tasks