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Faster and Better Grammar-based Text-to-SQL Parsing via Clause-level Parallel Decoding and Alignment Loss

arXiv 2204.12186 ↗First posted 26 Apr 2022cs.CLcited by 0

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Abstract (arXiv)

Grammar-based parsers have achieved high performance in the cross-domain text-to-SQL parsing task, but suffer from low decoding efficiency due to the much larger number of actions for grammar selection than that of tokens in SQL queries. Meanwhile, how to better align SQL clauses and question segments has been a key challenge for parsing performance. Therefore, this paper proposes clause-level parallel decoding and alignment loss to enhance two high-performance grammar-based parsers, i.e., RATSQL and LGESQL. Experimental results of two parsers show that our method obtains consistent improvements both in accuracy and decoding speed.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 20 references that are in the map. First posted on arXiv 26 Apr 2022.

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References · 13

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
  3. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 687
  4. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 424
  5. 2019Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representationcited by 210
  6. 2020GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsingcited by 171
  7. 2017SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learningcited by 135
  8. 2021LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relationscited by 119
  9. 2018Coarse-to-Fine Decoding for Neural Semantic Parsingcited by 110
  10. 2018TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generationcited by 88
  11. 2018TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generationcited by 81
  12. 2021Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsingcited by 33
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  1. 2020DuoRAT: Towards Simpler Text-to-SQL Modelscited by 17

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  2. 2026Beam Search, Self-Consistency, and the Limits of Inference-Time Scaling for Grammar-Constrained Text-to-SQL in Small Language Models
  3. 2022Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parsers
  4. 2024SQLFixAgent: Towards Semantic-Accurate Text-to-SQL Parsing via Consistency-Enhanced Multi-Agent Collaboration
  5. 2024Multi-Turn Interactions for Text-to-SQL with Large Language Models
  6. 2025SPFT-SQL: Enhancing Large Language Model for Text-to-SQL Parsing by Self-Play Fine-Tuning
  7. 2025CRED-SQL: Enhancing Real-world Large Scale Database Text-to-SQL Parsing through Cluster Retrieval and Execution Description
  8. 2026DecoSearch: Complexity-Aware Routing and Plan-Level Repair for Text-to-SQL