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LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations

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arxiv 2106.01093 v3 pith:3PIA4SVE submitted 2021-06-02 cs.CL

Ruisheng Cao , Lu Chen , Zhi Chen , Yanbin Zhao , Su Zhu , Kai Yu This is my paper
classification cs.CL
keywords graphtext-to-sqllinelocalnon-localrelationsedgesenhanced
verification ladder T0 review T1 audit T2 compute T3 formal
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This work aims to tackle the challenging heterogeneous graph encoding problem in the text-to-SQL task. Previous methods are typically node-centric and merely utilize different weight matrices to parameterize edge types, which 1) ignore the rich semantics embedded in the topological structure of edges, and 2) fail to distinguish local and non-local relations for each node. To this end, we propose a Line Graph Enhanced Text-to-SQL (LGESQL) model to mine the underlying relational features without constructing meta-paths. By virtue of the line graph, messages propagate more efficiently through not only connections between nodes, but also the topology of directed edges. Furthermore, both local and non-local relations are integrated distinctively during the graph iteration. We also design an auxiliary task called graph pruning to improve the discriminative capability of the encoder. Our framework achieves state-of-the-art results (62.8% with Glove, 72.0% with Electra) on the cross-domain text-to-SQL benchmark Spider at the time of writing.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SDE-SQL: Enhancing Text-to-SQL Generation in Large Language Models via Self-Driven Exploration with SQL Probes

    cs.CL 2025-06 conditional novelty 6.0 of 10

    SDE-SQL improves text-to-SQL accuracy by having the model generate and execute exploratory SQL probes to learn database contents before and while writing the final query.

  2. Pi-SQL: Enhancing Text-to-SQL with Fine-Grained Guidance from Pivot Programming Languages

    cs.CL 2025-06 conditional novelty 5.0 of 10

    Generating Python programs as intermediate guidance before SQL, then voting on Python execution results to select the fastest matching SQL, improves text-to-SQL execution accuracy and efficiency on BIRD and Archer.

  3. Confidence Estimation for Text-to-SQL in Large Language Models

    cs.CL 2025-08 unverdicted novelty 4.0 of 10

    Consistency-based methods are the most reliable confidence signal for text-to-SQL in black-box LLMs, and executing queries against a database adds a useful correctness signal.

  4. Interactive Text-to-SQL via Expected Information Gain for Disambiguation

    cs.DB 2025-07 reject novelty 4.0 of 10

    An interactive text-to-SQL framework selects clarification questions by expected information gain over a distribution of candidate SQL queries.

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