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 overlook 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, \textit{i.e.}, the performance decreases substantially as
the number of graph layers increases. To cope with these challenges, we propose
a novel \textbf{Dep}th-\textbf{Wi}se \textbf{G}raph \textbf{N}eural
\textbf{N}etwork (\textbf{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.

我们提出了一种新的深度分层图神经网络（DepWiGNN）用于空间推理，在两个具有多跳空间推理数据集上的实验证明 DepWiGNN 优于现有的空间推理方法，并与其他三种图神经网络进行了比较来展示其在图中捕捉长依赖性方面的优势。