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RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers

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arxiv 1911.04942 v5 pith:OYSBL5TG submitted 2019-11-10 cs.CL cs.AI

Bailin Wang , Richard Shin , Xiaodong Liu , Oleksandr Polozov , Matthew Richardson This is my paper · ORCID
classification cs.CLcs.AI
keywords databaseschemaencodinglinkingalignmentframeworkquestionsrat-sql
verification ladder T0 review T1 audit T2 compute T3 formal
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When translating natural language questions into SQL queries to answer questions from a database, contemporary semantic parsing models struggle to generalize to unseen database schemas. The generalization challenge lies in (a) encoding the database relations in an accessible way for the semantic parser, and (b) modeling alignment between database columns and their mentions in a given query. We present a unified framework, based on the relation-aware self-attention mechanism, to address schema encoding, schema linking, and feature representation within a text-to-SQL encoder. On the challenging Spider dataset this framework boosts the exact match accuracy to 57.2%, surpassing its best counterparts by 8.7% absolute improvement. Further augmented with BERT, it achieves the new state-of-the-art performance of 65.6% on the Spider leaderboard. In addition, we observe qualitative improvements in the model's understanding of schema linking and alignment. Our implementation will be open-sourced at https://github.com/Microsoft/rat-sql.

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Forward citations

Cited by 6 Pith papers

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

  1. TailorSQL: An NL2SQL System Tailored to Your Query Workload

    cs.DB 2025-05 conditional novelty 6.0 of 10

    Adding workload-derived hints and tailored retrieval from past SQL queries improves NL2SQL execution accuracy and reduces prompt size on three benchmarks.

  2. ODIN: A NL2SQL Recommender to Handle Schema Ambiguity

    cs.DB 2025-05 conditional novelty 6.0 of 10

    ODIN uses schema masking, conformal-prediction filtering, and user-preference hints to return a small set of SQL queries that includes the correct one more often than baseline sampling on ambiguous-schema benchmarks.

  3. PaVeRL-SQL: Text-to-SQL via Partial-Match Rewards and Verbal Reinforcement Learning

    cs.AI 2025-09 reject novelty 5.0 of 10

    PaVeRL-SQL reports SOTA execution accuracy on Spider2.0-SQLite using partial-match rewards and verbal RL, but overclaims SOTA on Spider and BIRD.

  4. 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.

  5. 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.

  6. eSapiens: A Real-World NLP Framework for Multimodal Document Understanding and Enterprise Knowledge Processing

    cs.IR 2025-06 conditional novelty 3.0 of 10

    eSapiens combines text-to-SQL and hybrid retrieval-augmented generation with citation verification, and its evaluation on RAGTruth shows better contextual relevance than a FAISS baseline across most LLMs, at the cost ...

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