핵심 개념
DepWiGNN introduces a novel approach for multi-hop spatial reasoning in text, focusing on depth-wise propagation in graphs to capture long dependencies effectively.
초록
DepWiGNN proposes a Depth-Wise Graph Neural Network to address challenges in multi-hop spatial reasoning. The model utilizes a novel node memory scheme and depth-wise aggregation to collect long dependencies without over-smoothing. Experimental results show superior performance compared to existing methods.
Introduction
Spatial reasoning in text is crucial for various applications.
Existing approaches overlook the gap between natural language and symbolic structures.
Method
DepWiGNN operates over the depth dimension of the graph.
Node memory initialization, long dependency collection, and spatial relation retrieval are key components.
Experiments
DepWiGNN outperforms existing spatial reasoning methods on challenging datasets.
Comparisons with other GNNs highlight its ability to capture long dependencies effectively.
Conclusion
DepWiGNN offers a novel solution for multi-hop spatial reasoning, demonstrating immunity to over-smoothing and superior performance.
통계
Graph neural networks have been used in multi-hop reasoning.
Existing methods for multi-hop reasoning face challenges with over-smoothing.
DepWiGNN outperforms existing spatial reasoning methods on challenging datasets.
인용구
"Graph neural networks have been considerably used in multi-hop reasoning."
"DepWiGNN excels in multi-hop spatial reasoning tasks."