Khái niệm cốt lõi
DepWiGNN introduces a novel approach for multi-hop spatial reasoning in text, focusing on depth-wise propagation in graphs to capture long dependencies effectively.
Tóm tắt
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.
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Introduction
- Spatial reasoning in text is crucial for various applications.
- Existing approaches overlook the gap between natural language and symbolic structures.
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Method
- DepWiGNN operates over the depth dimension of the graph.
- Node memory initialization, long dependency collection, and spatial relation retrieval are key components.
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Experiments
- DepWiGNN outperforms existing spatial reasoning methods on challenging datasets.
- Comparisons with other GNNs highlight its ability to capture long dependencies effectively.
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Conclusion
- DepWiGNN offers a novel solution for multi-hop spatial reasoning, demonstrating immunity to over-smoothing and superior performance.
Thống kê
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.
Trích dẫn
"Graph neural networks have been considerably used in multi-hop reasoning."
"DepWiGNN excels in multi-hop spatial reasoning tasks."