Before revision:  On two natural language tasks across three datasets, ZSL-KG shows an average improvement of 9.2 points of accuracy versus state-of-the-art methods. In addition, on an object classification task, ZSL-KG shows a 2.2 accuracy point improvement versus the best methods that do not require hand-engineered class representations. Finally, we find that ZSL-KG outperforms the best performing  graph neural networks with linear aggregators by an average of 3.8 points of accuracy across these four datasets .
After revision:  On two natural language tasks across three datasets, ZSL-KG outperforms the best performing graph neural networks with linear aggregators by an average of 3.8 points of accuracy across these four datasets .