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Improving Text-to-SQL with Schema Dependency Learning

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arxiv 2103.04399 v2 pith:AOHK744F submitted 2021-03-07 cs.CL

Binyuan Hui , Xiang Shi , Ruiying Geng , Binhua Li , Yongbin Li , Jian Sun , Xiaodan Zhu This is my paper
classification cs.CL
keywords dependencyschematext-to-sqlapplicationsdecodingexecution-guidedinferencelearning
verification ladder T0 review T1 audit T2 compute T3 formal
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Text-to-SQL aims to map natural language questions to SQL queries. The sketch-based method combined with execution-guided (EG) decoding strategy has shown a strong performance on the WikiSQL benchmark. However, execution-guided decoding relies on database execution, which significantly slows down the inference process and is hence unsatisfactory for many real-world applications. In this paper, we present the Schema Dependency guided multi-task Text-to-SQL model (SDSQL) to guide the network to effectively capture the interactions between questions and schemas. The proposed model outperforms all existing methods in both the settings with or without EG. We show the schema dependency learning partially cover the benefit from EG and alleviates the need for it. SDSQL without EG significantly reduces time consumption during inference, sacrificing only a small amount of performance and provides more flexibility for downstream applications.

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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. The Nuts and Bolts of Natural Language to SQL Translation: A Systematic Analysis of Model Pipeline Optimisation Approaches and their Interactions

    cs.CL 2026-07 conditional novelty 6.0 of 10

    Ablation and Shapley analysis of NatSQL, GAZP synthetic fine-tuning, token preprocessing, and a novel beam reranker on SmBoP and RASAT show that component interactions—not full stacking—drive the best lightweight NL2S...

  2. 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.

  3. CHS-SQL: A Text-to-SQL approach based on Confidence-Guided Heuristic Search Schema Linking process

    cs.AI 2026-06 conditional novelty 4.0 of 10

    CHS-SQL combines beam search with model-internal-confidence filtering in schema linking to improve small-model text-to-SQL accuracy on Spider and BIRD.

  4. Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities

    cs.CL 2025-05 conditional novelty 4.0 of 10

    A systematic review organizing LLM-based text-to-SQL methods into pre-processing, in-context learning, fine-tuning, and post-processing paradigms, with a catalog of datasets, metrics, challenges, and future directions.

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