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.

翻译：文本中的空间推理在各类实际应用中扮演着关键角色。现有空间推理方法通常从纯文本中推断空间关系，却忽视了自然语言与符号结构之间的鸿沟。图神经网络在归纳和聚合符号结构方面展现出卓越能力。然而，经典图神经网络因过平滑问题（即随着图层级增加性能显著下降）难以处理多跳空间推理。为应对这些挑战，我们提出一种新颖的**深**度维度**图**神经**网**络（**DepWiGNN**）。具体而言，我们设计了一种新型节点记忆方案，沿图的深度维度而非广度维度聚合信息，从而无需堆叠多层即可获取长距离依赖关系。在两个具有挑战性的多跳空间推理数据集上的实验结果表明，DepWiGNN优于现有空间推理方法。与其他三种图神经网络的对比进一步证明了其在捕获图中长距离依赖方面的优越性。