Before revision:  Whereas most prior work on graph neural networks uses linear functions to aggregate information from neighboring nodes, we find that non-linear aggregators such as LSTMs or transformers lead to significant improvements on zero-shot tasks. 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.
After revision:  Whereas most prior work on graph neural networks uses linear functions to aggregate information from neighboring nodes, we find that non-linear aggregators such as LSTMs or transformers lead to significant improvements on zero-shot learning tasks in language and vision. Our results show ZSL-KG shows an average improvement of 9.2 points of accuracy versus state-of-the-art methods.