DepWiGNN: A depth-wise graph neural network for multi-hop spatial reasoning in text

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 exceptio...

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Bibliographic Details
Main Authors: LI, Shuaiyi, DENG, Yang, LAM, Wai
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2023
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Online Access:https://ink.library.smu.edu.sg/sis_research/9119
https://ink.library.smu.edu.sg/context/sis_research/article/10122/viewcontent/2023.findings_emnlp.428.pdf
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Institution: Singapore Management University
Language: English
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Summary: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, 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.