DepWiGNN: A Depth-wise Graph Neural Network for Multi-hop Spatial Reasoning in Text

arXiv:2310.12557v2 Announce Type: replace
Abstract: Spatial reasoning in text plays a crucial role in various real-world applications. Existing approaches for spatial reasoning typically infer spatial relations from pure text, which overlooks the gap between natural language and symbolic structures. Graph neural networks (GNNs) have showcased exceptional proficiency in inducing and aggregating symbolic structures. However, classical GNNs face challenges in handling multi-hop spatial reasoning due to the over-smoothing issue, i.e., the performance decreases substantially as the number of graph layers increases. …

abstract applications arxiv cs.ai cs.cl gap gnns graph graph neural network graph neural networks language natural natural language network networks neural network neural networks reasoning relations role spatial text type wise world