mlsanityData to 14 Oct 2026

Pay More Attention to History: A Context Modelling Strategy for Conversational Text-to-SQL

arXiv 2112.08735 ↗First posted 16 Dec 2021cs.CL cs.AIcited by 5

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Abstract (arXiv)

Conversational text-to-SQL aims at converting multi-turn natural language queries into their corresponding SQL (Structured Query Language) representations. One of the most intractable problems of conversational text-to-SQL is modelling the semantics of multi-turn queries and gathering the proper information required for the current query. This paper shows that explicitly modelling the semantic changes by adding each turn and the summarization of the whole context can bring better performance on converting conversational queries into SQLs. In particular, we propose two conversational modelling tasks in both turn grain and conversation grain. These two tasks simply work as auxiliary training tasks to help with multi-turn conversational semantic parsing. We conducted empirical studies and achieved new state-of-the-art results on the large-scale open-domain conversational text-to-SQL dataset. The results demonstrate that the proposed mechanism significantly improves the performance of multi-turn semantic parsing.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 31 of its 40 references that are in the map. First posted on arXiv 16 Dec 2021.

Builds on

31 of 40 references.

References · 31

  1. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
  2. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 687
  3. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 424
  4. 2019Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representationcited by 210
  5. 2019CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databasescited by 185
  6. 2016Language to Logical Form with Neural Attentioncited by 158
  7. 2019Good-Enough Compositional Data Augmentationcited by 158
  8. 2019SParC: Cross-Domain Semantic Parsing in Contextcited by 144
  9. 2017SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learningcited by 135
  10. 2021LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relationscited by 119
  11. 2016Data Recombination for Neural Semantic Parsingcited by 111
  12. 2018Coarse-to-Fine Decoding for Neural Semantic Parsingcited by 110
19 more
  1. 2019A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualizationcited by 88
  2. 2018TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generationcited by 81
  3. 2019Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questionscited by 80
  4. 2020Grounded Adaptation for Zero-shot Executable Semantic Parsingcited by 76
  5. 2018SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Taskcited by 74
  6. 2019Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsingcited by 74
  7. 2020Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Trainingcited by 65
  8. 2021Unlocking Compositional Generalization in Pre-trained Models Using Intermediate Representationscited by 55
  9. 2019Global Reasoning over Database Structures for Text-to-SQL Parsingcited by 43
  10. 2021Learning to Synthesize Data for Semantic Parsingcited by 41
  11. 2021ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parsercited by 35
  12. 2018Confidence Modeling for Neural Semantic Parsingcited by 33
  13. 2020Hybrid Ranking Network for Text-to-SQLcited by 33
  14. 2020IGSQL: Database Schema Interaction Graph Based Neural Model for Context-Dependent Text-to-SQL Generationcited by 29
  15. 2020Tracking Interaction States for Multi-Turn Text-to-SQL Semantic Parsingcited by 18
  16. 2018Semantic Parsing with Syntax- and Table-Aware SQL Generationcited by 17
  17. 2019Content Enhanced BERT-based Text-to-SQL Generationcited by 16
  18. 2017Learning Structured Natural Language Representations for Semantic Parsingcited by 15
  19. 2021Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsingcited by 15

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Cited by year

Citing papers per year: 2022: 3; 2023: 1; 2024: 1.

5 papers.

  1. 2022RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQLcited by 85
  2. 2022CQR-SQL: Conversational Question Reformulation Enhanced Context-Dependent Text-to-SQL Parserscited by 11
  3. 2022MIGA: A Unified Multi-task Generation Framework for Conversational Text-to-SQLcited by 5
  4. 2024EHR-SeqSQL : A Sequential Text-to-SQL Dataset For Interactively Exploring Electronic Health Recordscited by 5
  5. 2023QURG: Question Rewriting Guided Context-Dependent Text-to-SQL Semantic Parsingcited by 1