RHB-Net: A Relation-aware Historical Bridging Network for Text2SQL Auto-Completion
Authors: 
Bolong Zheng, Lei Bi, Ruijie Xi, Lu Chen, Yunjun Gao, Xiaofang Zhou, 
Christian S. JensenAuthors Info & Claims
Bolong Zheng, Lei Bi, Ruijie Xi, Lu Chen, Yunjun Gao, Xiaofang Zhou, Christian S. JensenAuthors Info & ClaimsPages 1458 - 1467
Abstract
Test2SQL, a natural language interface to database querying, has seen considerable improvement, in part due to advances in deep learning. However, despite recent improvement, existing Text2SQL proposals allow only input in the form of complete questions. This leaves behind users who struggle to formulate complete questions, e.g., because they lack database expertise or are unfamiliar with the underlying database schema. To address this shortcoming, we study the novel problem of Text2SQL Auto-Completion (TSAC) that extends Text2SQL to also take partial or incomplete questions as input. Specifically, the TSAC problem is to predict the complete, executable SQL query. To solve the problem, we propose a novel Relation-aware Historical Bridging Network (RHB-Net) that consists of a relation-aware union encoder and an extraction-generation sensitive decoder. RHB-Net models relations between questions and database schemas and predicts the ambiguous intents expressed in partial queries. We also propose two optimization strategies: historical query bridging that fuses historical database queries, and a dynamic context construction that prevents repeated generation of the same SQL elements. Extensive experiments with real-world data offer evidence that RHB-Net is capable of outperforming baseline algorithms.
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Index Terms
- RHB-Net: A Relation-aware Historical Bridging Network for Text2SQL Auto-Completion
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3567 pages
ISBN:9781450394086
DOI:10.1145/3539618
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Published: 18 July 2023
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