  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. To cope with these challenges, we propose a
novel Depth-Wise Graph Neural Network (DepWiGNN). Specifically, we design a
novel node memory scheme and aggregate the information over the depth dimension
instead of the breadth dimension of the graph, which empowers the ability to
collect long dependencies without stacking multiple layers. Experimental
results on two challenging multi-hop spatial reasoning datasets show that
DepWiGNN outperforms existing spatial reasoning methods. The comparisons with
the other three GNNs further demonstrate its superiority in capturing long
dependency in the graph.
