Decoupling SQL Query Hardness Parsing for Text-to-SQL
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Abstract (arXiv)
The fundamental goal of the Text-to-SQL task is to translate natural language question into SQL query. Current research primarily emphasizes the information coupling between natural language questions and schemas, and significant progress has been made in this area. The natural language questions as the primary task requirements source determines the hardness of correspond SQL queries, the correlation between the two always be ignored. However, when the correlation between questions and queries was decoupled, it may simplify the task. In this paper, we introduce an innovative framework for Text-to-SQL based on decoupling SQL query hardness parsing. This framework decouples the Text-to-SQL task based on query hardness by analyzing questions and schemas, simplifying the multi-hardness task into a single-hardness challenge. This greatly reduces the parsing pressure on the language model. We evaluate our proposed framework and achieve a new state-of-the-art performance of fine-turning methods on Spider dev.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 28 of its 41 references that are in the map. First posted on arXiv 11 Dec 2023.
Builds on
28 of 41 references.
References · 28
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
- 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
- 2018Adafactor: Adaptive Learning Rates with Sublinear Memory Costcited by 896
- 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 679
- 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 420
- 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 402
- 2023RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQLcited by 191
- 2020GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsingcited by 171
- 2017SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learningcited by 135
16 more
- 2020Semantic Evaluation for Text-to-SQL with Distilled Test Suitescited by 123
- 2021LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relationscited by 119
- 2020Relational Graph Attention Network for Aspect-based Sentiment Analysiscited by 104
- 2020When Do You Need Billions of Words of Pretraining Data?cited by 104
- 2017Learning a Neural Semantic Parser from User Feedbackcited by 98
- 2020SmBoP: Semi-autoregressive Bottom-up Semantic Parsingcited by 97
- 2019A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualizationcited by 88
- 2022RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQLcited by 85
- 2018SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Taskcited by 74
- 2021Natural SQL: Making SQL Easier to Infer from Natural Language Specificationscited by 66
- 2020Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Trainingcited by 65
- 2018Robust Text-to-SQL Generation with Execution-Guided Decodingcited by 51
- 2019Global Reasoning over Database Structures for Text-to-SQL Parsingcited by 43
- 2021SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQLcited by 43
- 2022S2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parserscited by 41
- 2022Towards Generalizable and Robust Text-to-SQL Parsingcited by 7
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- 2024RH-SQL: Refined Schema and Hardness Prompt for Text-to-SQL13 Jun 2024