# IGSQL: Database Schema Interaction Graph Based Neural Model for Context-Dependent Text-to-SQL Generation

arXiv: https://arxiv.org/abs/2011.05744 · HTML: https://mlsanity.com/paper/2011.05744/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2020.

## Built on by (most cited first, 29 of 29)

- [RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL](https://mlsanity.com/paper/2205.06983/) (2022)
- [A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions](https://mlsanity.com/paper/2208.13629/) (2022)
- [Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect](https://mlsanity.com/paper/2208.10099/) (2022)
- [ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser](https://mlsanity.com/paper/2104.04689/) (2021)
- [Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation](https://mlsanity.com/paper/2212.09994/) (2022)
- [HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsing](https://mlsanity.com/paper/2203.07376/) (2022)
- [UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL](https://mlsanity.com/paper/2203.07781/) (2022)
- [STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing](https://mlsanity.com/paper/2210.11888/) (2022)
- [Interactive Text-to-SQL Generation via Editable Step-by-Step Explanations](https://mlsanity.com/paper/2305.07372/) (2023)
- [Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL](https://mlsanity.com/paper/2106.02282/) (2021)
- [CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-Editions](https://mlsanity.com/paper/2405.02712/) (2024)
- [CQR-SQL: Conversational Question Reformulation Enhanced Context-Dependent Text-to-SQL Parsers](https://mlsanity.com/paper/2205.07686/) (2022)
- [Towards Generalizable and Robust Text-to-SQL Parsing](https://mlsanity.com/paper/2210.12674/) (2022)
- [Pay More Attention to History: A Context Modelling Strategy for Conversational Text-to-SQL](https://mlsanity.com/paper/2112.08735/) (2021)
- [MIGA: A Unified Multi-task Generation Framework for Conversational Text-to-SQL](https://mlsanity.com/paper/2212.09278/) (2022)
- [MTSQL-R1: Towards Long-Horizon Multi-Turn Text-to-SQL via Agentic Training](https://mlsanity.com/paper/2510.12831/) (2025)
- [SQLformer: Deep Auto-Regressive Query Graph Generation for Text-to-SQL Translation](https://mlsanity.com/paper/2310.18376/) (2023)
- [Conversational Semantic Parsing using Dynamic Context Graphs](https://mlsanity.com/paper/2305.06164/) (2023)
- [SPSQL: Step-by-step Parsing Based Framework for Text-to-SQL Generation](https://mlsanity.com/paper/2305.11061/) (2023)
- [Exploring the Compositional Generalization in Context Dependent Text-to-SQL Parsing](https://mlsanity.com/paper/2306.04480/) (2023)
- [T5-SR: A Unified Seq-to-Seq Decoding Strategy for Semantic Parsing](https://mlsanity.com/paper/2306.08368/) (2023)
- [ASTormer: An AST Structure-aware Transformer Decoder for Text-to-SQL](https://mlsanity.com/paper/2310.18662/) (2023)
- [Linking-Enhanced Pre-Training for Table Semantic Parsing](https://mlsanity.com/paper/2111.09486/) (2021)
- [Controllable Data Augmentation for Context-Dependent Text-to-SQL](https://mlsanity.com/paper/2304.13902/) (2023)
- [QURG: Question Rewriting Guided Context-Dependent Text-to-SQL Semantic Parsing](https://mlsanity.com/paper/2305.06655/) (2023)
- [SeSQL: Yet Another Large-scale Session-level Chinese Text-to-SQL Dataset](https://mlsanity.com/paper/2208.12711/) (2022)
- [Conversational Text-to-SQL: An Odyssey into State-of-the-Art and Challenges Ahead](https://mlsanity.com/paper/2302.11054/) (2023)
- [DecoSearch: Complexity-Aware Routing and Plan-Level Repair for Text-to-SQL](https://mlsanity.com/paper/2606.17821/) (2026)
- [DRL: A Deterministic Relational Middleware Layer for Transaction-Safe Enterprise NL2SQL Under Schema-Graph Scaling](https://mlsanity.com/paper/2608.26172/) (2026)

## Abstract (arXiv)

Context-dependent text-to-SQL task has drawn much attention in recent years. Previous models on context-dependent text-to-SQL task only concentrate on utilizing historical user inputs. In this work, in addition to using encoders to capture historical information of user inputs, we propose a database schema interaction graph encoder to utilize historicalal information of database schema items. In decoding phase, we introduce a gate mechanism to weigh the importance of different vocabularies and then make the prediction of SQL tokens. We evaluate our model on the benchmark SParC and CoSQL datasets, which are two large complex context-dependent cross-domain text-to-SQL datasets. Our model outperforms previous state-of-the-art model by a large margin and achieves new state-of-the-art results on the two datasets. The comparison and ablation results demonstrate the efficacy of our model and the usefulness of the database schema interaction graph encoder.