Abstract
Text-To-SQL is crucial for enabling users without technical expertise to effectively extract important information from databases. The graph-based encoder has been successfully employed in this field. However, existing methods often adopt a node-centric approach, focusing on single-hop edge relations. This approach gives rise to two main issues: 1) failure to differentiate between single-hop and multi-hop relations among nodes; 2) ignoring the valuable multi-hop reasoning information between nodes. To tackle these challenges, we propose a Graph Attention Diffusion Enhanced Text-To-SQL(GADESQL) model that enables multi-hop reasoning among nodes. With GAD, information can propagate efficiently through multi-hop paths, uniquely integrating single-hop and multi-hop relations during the graph iteration process. Furthermore, we employ Semantic Dependency Parsing for natural language analysis, constructing a semantic analysis tree for questions to enhance the effective connection between question tokens and Schema structures. Experiments on the cross-domain dataset Spider demonstrate that our model possesses strong generalization capabilities, achieving certain performance improvements over existing works.
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Acknowledgment
This work is supported by the National Science Fund of China under grant No. 62072075 and the Key research and development projects of Sichuan Provincial Science and Technology Plan Project under grant No. 2023YFS0420.
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Cao, Q., Xi, R., Wu, J., Nie, X., Liu, Y., Hou, M. (2023). GADESQL: Graph Attention Diffusion Enhanced Text-To-SQL with Single and Multi-hop Relations. In: Zhang, F., Wang, H., Barhamgi, M., Chen, L., Zhou, R. (eds) Web Information Systems Engineering – WISE 2023. WISE 2023. Lecture Notes in Computer Science, vol 14306. Springer, Singapore. https://doi.org/10.1007/978-981-99-7254-8_53
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DOI: https://doi.org/10.1007/978-981-99-7254-8_53
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