mlsanityData to 7 Oct 2026

An Investigation Between Schema Linking and Text-to-SQL Performance

arXiv 2102.01847 ↗First posted 3 Feb 2021cs.CLcited by 5

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Abstract (arXiv)

Text-to-SQL is a crucial task toward developing methods for understanding natural language by computers. Recent neural approaches deliver excellent performance; however, models that are difficult to interpret inhibit future developments. Hence, this study aims to provide a better approach toward the interpretation of neural models. We hypothesize that the internal behavior of models at hand becomes much easier to analyze if we identify the detailed performance of schema linking simultaneously as the additional information of the text-to-SQL performance. We provide the ground-truth annotation of schema linking information onto the Spider dataset. We demonstrate the usefulness of the annotated data and how to analyze the current state-of-the-art neural models.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 12 of its 31 references that are in the map. First posted on arXiv 3 Feb 2021.

Builds on

12 of 31 references.

References · 12

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  3. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  4. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 679
  5. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 420
  6. 2019Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representationcited by 210
  7. 2018Improving Text-to-SQL Evaluation Methodologycited by 133
  8. 2018TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generationcited by 81
  9. 2019Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questionscited by 80
  10. 2019Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsingcited by 74
  11. 2020RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databasescited by 63
  12. 2019Global Reasoning over Database Structures for Text-to-SQL Parsingcited by 43

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Cited by year (2026 to 7 Oct)

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

5 papers.

  1. 2022Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expectcited by 47
  2. 2022Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking Graphcited by 22
  3. 2023Benchmarking Diverse-Modal Entity Linking with Generative Modelscited by 5
  4. 2025DynaQuery: A Self-Adapting Framework for Querying Structured and Multimodal Datacited by 0
  5. 2026CHS-SQL: A Text-to-SQL approach based on Confidence-Guided Heuristic Search Schema Linking processcited by 0