mlsanityData to 7 Oct 2026

Relation Aware Semi-autoregressive Semantic Parsing for NL2SQL

arXiv 2108.00804 ↗First posted 2 Aug 2021cs.CL cs.AIcited by 8

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Abstract (arXiv)

Natural language to SQL (NL2SQL) aims to parse a natural language with a given database into a SQL query, which widely appears in practical Internet applications. Jointly encode database schema and question utterance is a difficult but important task in NL2SQL. One solution is to treat the input as a heterogeneous graph. However, it failed to learn good word representation in question utterance. Learning better word representation is important for constructing a well-designed NL2SQL system. To solve the challenging task, we present a Relation aware Semi-autogressive Semantic Parsing (MODN) ~framework, which is more adaptable for NL2SQL. It first learns relation embedding over the schema entities and question words with predefined schema relations with ELECTRA and relation aware transformer layer as backbone. Then we decode the query SQL with a semi-autoregressive parser and predefined SQL syntax. From empirical results and case study, our model shows its effectiveness in learning better word representation in NL2SQL.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 12 of its 20 references that are in the map. First posted on arXiv 2 Aug 2021.

Builds on

12 of 20 references.

References · 12

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  3. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 420
  4. 2019Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representationcited by 210
  5. 2020GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsingcited by 171
  6. 2017SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learningcited by 135
  7. 2020Structure-Grounded Pretraining for Text-to-SQLcited by 126
  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. 2020SmBoP: Semi-autoregressive Bottom-up Semantic Parsingcited by 97
  11. 2020Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Trainingcited by 65
  12. 2020Optimizing Deeper Transformers on Small Datasetscited by 41

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 3; 2023: 1; 2024: 2; 2025: 0; 2026: 1.

8 papers.

  1. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 465
  2. 2024A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?cited by 54
  3. 2022A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directionscited by 51
  4. 2022T5QL: Taming language models for SQL generationcited by 8
  5. 2022SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parserscited by 7
  6. 2021Prefix-to-SQL: Text-to-SQL Generation from Incomplete User Questionscited by 2
  7. 2024Enhancing Text-to-SQL Capabilities of Large Language Models via Domain Database Knowledge Injectioncited by 2
  8. 2026CHS-SQL: A Text-to-SQL approach based on Confidence-Guided Heuristic Search Schema Linking processcited by 0