# S2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers

arXiv: https://arxiv.org/abs/2203.06958 · HTML: https://mlsanity.com/paper/2203.06958/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 42 references that are in the map.
- First posted on arXiv 14 Mar 2022.

## Builds on (most cited first, 29 of 29)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers](https://mlsanity.com/paper/1911.04942/) (2019)
- [PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://mlsanity.com/paper/2109.05093/) (2021)
- [TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data](https://mlsanity.com/paper/2005.08314/) (2020)
- [Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation](https://mlsanity.com/paper/1905.08205/) (2019)
- [GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing](https://mlsanity.com/paper/2009.13845/) (2020)
- [Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing](https://mlsanity.com/paper/2012.12627/) (2020)
- [Structure-Grounded Pretraining for Text-to-SQL](https://mlsanity.com/paper/2010.12773/) (2020)
- [LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations](https://mlsanity.com/paper/2106.01093/) (2021)
- [Towards Robustness of Text-to-SQL Models against Synonym Substitution](https://mlsanity.com/paper/2106.01065/) (2021)
- [Learning a Neural Semantic Parser from User Feedback](https://mlsanity.com/paper/1704.08760/) (2017)
- [SmBoP: Semi-autoregressive Bottom-up Semantic Parsing](https://mlsanity.com/paper/2010.12412/) (2020)
- [Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions](https://mlsanity.com/paper/1909.00786/) (2019)
- [Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training](https://mlsanity.com/paper/2012.10309/) (2020)
- [RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databases](https://mlsanity.com/paper/2004.03125/) (2020)
- [Global Reasoning over Database Structures for Text-to-SQL Parsing](https://mlsanity.com/paper/1908.11214/) (2019)
- [Meta-Learning for Domain Generalization in Semantic Parsing](https://mlsanity.com/paper/2010.11988/) (2020)
- [Learning to Synthesize Data for Semantic Parsing](https://mlsanity.com/paper/2104.05827/) (2021)
- [ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser](https://mlsanity.com/paper/2104.04689/) (2021)
- [Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs](https://mlsanity.com/paper/1909.04165/) (2019)
- [Improving Text-to-SQL with Schema Dependency Learning](https://mlsanity.com/paper/2103.04399/) (2021)
- [Bertrand-DR: Improving Text-to-SQL using a Discriminative Re-ranker](https://mlsanity.com/paper/2002.00557/) (2020)
- [Dating Documents using Graph Convolution Networks](https://mlsanity.com/paper/1902.00175/) (2019)
- [Syntax-Enhanced Self-Attention-Based Semantic Role Labeling](https://mlsanity.com/paper/1910.11204/) (2019)
- [Self-Orthogonality Module: A Network Architecture Plug-in for Learning Orthogonal Filters](https://mlsanity.com/paper/2001.01275/) (2020)

## Built on by (most cited first, 30 of 41)

- [Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation](https://mlsanity.com/paper/2308.15363/) (2023)
- [RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL](https://mlsanity.com/paper/2302.05965/) (2023)
- [Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning](https://mlsanity.com/paper/2301.13808/) (2023)
- [Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing](https://mlsanity.com/paper/2301.07507/) (2023)
- [RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL](https://mlsanity.com/paper/2205.06983/) (2022)
- [A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?](https://mlsanity.com/paper/2408.05109/) (2024)
- [Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect](https://mlsanity.com/paper/2208.10099/) (2022)
- [SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended)](https://mlsanity.com/paper/2306.00739/) (2023)
- [PURPLE: Making a Large Language Model a Better SQL Writer](https://mlsanity.com/paper/2403.20014/) (2024)
- [Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing](https://mlsanity.com/paper/2206.14017/) (2022)
- [Duplex Conversation: Towards Human-like Interaction in Spoken Dialogue Systems](https://mlsanity.com/paper/2205.15060/) (2022)
- [Importance of Synthesizing High-quality Data for Text-to-SQL Parsing](https://mlsanity.com/paper/2212.08785/) (2022)
- [Reliable Text-to-SQL with Adaptive Abstention](https://mlsanity.com/paper/2501.10858/) (2025)
- [STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing](https://mlsanity.com/paper/2210.11888/) (2022)
- [Prompting GPT-3.5 for Text-to-SQL with De-semanticization and Skeleton Retrieval](https://mlsanity.com/paper/2304.13301/) (2023)
- [UniRPG: Unified Discrete Reasoning over Table and Text as Program Generation](https://mlsanity.com/paper/2210.08249/) (2022)
- [Retrieval-augmented GPT-3.5-based Text-to-SQL Framework with Sample-aware Prompting and Dynamic Revision Chain](https://mlsanity.com/paper/2307.05074/) (2023)
- [SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers](https://mlsanity.com/paper/2209.06442/) (2022)
- [Towards Generalizable and Robust Text-to-SQL Parsing](https://mlsanity.com/paper/2210.12674/) (2022)
- [SPRING: Situated Conversation Agent Pretrained with Multimodal Questions from Incremental Layout Graph](https://mlsanity.com/paper/2301.01949/) (2023)
- [TIE: Topological Information Enhanced Structural Reading Comprehension on Web Pages](https://mlsanity.com/paper/2205.06435/) (2022)
- [HeLM: Highlighted Evidence augmented Language Model for Enhanced Table-to-Text Generation](https://mlsanity.com/paper/2311.08896/) (2023)
- [A Multilingual Translator to SQL with Database Schema Pruning to Improve Self-Attention](https://mlsanity.com/paper/2306.14256/) (2023)
- [Enhancing LLM Fine-tuning for Text-to-SQLs by SQL Quality Measurement](https://mlsanity.com/paper/2410.01869/) (2024)
- [Graph-to-Text Generation with Dynamic Structure Pruning](https://mlsanity.com/paper/2209.07258/) (2022)
- [Plan-then-Seam: Towards Efficient Table-to-Text Generation](https://mlsanity.com/paper/2302.05138/) (2023)
- [Conversational Semantic Parsing using Dynamic Context Graphs](https://mlsanity.com/paper/2305.06164/) (2023)
- [T5-SR: A Unified Seq-to-Seq Decoding Strategy for Semantic Parsing](https://mlsanity.com/paper/2306.08368/) (2023)
- [Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents](https://mlsanity.com/paper/2412.05850/) (2024)
- [End-to-End Text-to-SQL with Dataset Selection: Leveraging LLMs for Adaptive Query Generation](https://mlsanity.com/paper/2508.06387/) (2025)

## Abstract (arXiv)

The task of converting a natural language question into an executable SQL query, known as text-to-SQL, is an important branch of semantic parsing. The state-of-the-art graph-based encoder has been successfully used in this task but does not model the question syntax well. In this paper, we propose S2SQL, injecting Syntax to question-Schema graph encoder for Text-to-SQL parsers, which effectively leverages the syntactic dependency information of questions in text-to-SQL to improve the performance. We also employ the decoupling constraint to induce diverse relational edge embedding, which further improves the network's performance. Experiments on the Spider and robustness setting Spider-Syn demonstrate that the proposed approach outperforms all existing methods when pre-training models are used, resulting in a performance ranks first on the Spider leaderboard.