# SQL-to-Text Generation with Graph-to-Sequence Model

arXiv: https://arxiv.org/abs/1809.05255 · HTML: https://mlsanity.com/paper/1809.05255/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2018.

## Built on by (most cited first, 30 of 31)

- [Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation](https://mlsanity.com/paper/2308.15363/) (2023)
- [Retrieval-Augmented Generation for Code Summarization via Hybrid GNN](https://mlsanity.com/paper/2006.05405/) (2020)
- [Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction](https://mlsanity.com/paper/2110.09681/) (2021)
- [SQLBench: A Comprehensive Evaluation for Text-to-SQL Capabilities of Large Language Models](https://mlsanity.com/paper/2403.02951/) (2024)
- [Structural Information Preserving for Graph-to-Text Generation](https://mlsanity.com/paper/2102.06749/) (2021)
- [Logic-Consistency Text Generation from Semantic Parses](https://mlsanity.com/paper/2108.00577/) (2021)
- [Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges](https://mlsanity.com/paper/2405.15604/) (2024)
- [Metasql: A Generate-then-Rank Framework for Natural Language to SQL Translation](https://mlsanity.com/paper/2402.17144/) (2024)
- [Equipping Language Models with Tool Use Capability for Tabular Data Analysis in Finance](https://mlsanity.com/paper/2401.15328/) (2024)
- [Impact of Evaluation Methodologies on Code Summarization](https://mlsanity.com/paper/2108.09619/) (2021)
- [SQLucid: Grounding Natural Language Database Queries with Interactive Explanations](https://mlsanity.com/paper/2409.06178/) (2024)
- [Hybrid LLM/Rule-based Approaches to Business Insights Generation from Structured Data](https://mlsanity.com/paper/2404.15604/) (2024)
- [Text-to-SQL Domain Adaptation via Human-LLM Collaborative Data Annotation](https://mlsanity.com/paper/2502.15980/) (2025)
- [Open-SQL Framework: Enhancing Text-to-SQL on Open-source Large Language Models](https://mlsanity.com/paper/2405.06674/) (2024)
- [Planning with Logical Graph-based Language Model for Instruction Generation](https://mlsanity.com/paper/2308.13782/) (2023)
- [A Systematic Review of Data-to-Text NLG](https://mlsanity.com/paper/2402.08496/) (2024)
- [Improving Logical-Level Natural Language Generation with Topic-Conditioned Data Augmentation and Logical Form Generation](https://mlsanity.com/paper/2112.06240/) (2021)
- [KU-DMIS at EHRSQL 2024:Generating SQL query via question templatization in EHR](https://mlsanity.com/paper/2406.00014/) (2024)
- [MultiTEND: A Multilingual Benchmark for Natural Language to NoSQL Query Translation](https://mlsanity.com/paper/2502.11022/) (2025)
- [BenchPress: A Human-in-the-Loop Annotation System for Rapid Text-to-SQL Benchmark Curation](https://mlsanity.com/paper/2510.13853/) (2025)
- [How to Train Your Agent to Read and Write](https://mlsanity.com/paper/2101.00916/) (2021)
- [Evaluating NL2SQL via SQL2NL](https://mlsanity.com/paper/2509.04657/) (2025)
- [Turing: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface](https://mlsanity.com/paper/2106.04559/) (2021)
- [Reranking for Natural Language Generation from Logical Forms: A Study based on Large Language Models](https://mlsanity.com/paper/2309.12294/) (2023)
- [Semantic Captioning: Benchmark Dataset and Graph-Aware Few-Shot In-Context Learning for SQL2Text](https://mlsanity.com/paper/2501.03166/) (2025)
- [IFDID: Information Filter upon Diversity-Improved Decoding for Diversity-Faithfulness Tradeoff in NLG](https://mlsanity.com/paper/2210.13829/) (2022)
- [A Graph-to-Text Approach to Knowledge-Grounded Response Generation in Human-Robot Interaction](https://mlsanity.com/paper/2311.16137/) (2023)
- [EzSQL: An SQL intermediate representation for improving SQL-to-text Generation](https://mlsanity.com/paper/2411.18923/) (2024)
- [Harnessing Temporal Databases for Systematic Evaluation of Factual Time-Sensitive Question-Answering in Large Language Models](https://mlsanity.com/paper/2508.02045/) (2025)
- [SPARTA: Scalable and Principled Benchmark of Tree-Structured Multi-hop QA over Text and Tables](https://mlsanity.com/paper/2602.23286/) (2026)

## Abstract (arXiv)

Previous work approaches the SQL-to-text generation task using vanilla Seq2Seq models, which may not fully capture the inherent graph-structured information in SQL query. In this paper, we first introduce a strategy to represent the SQL query as a directed graph and then employ a graph-to-sequence model to encode the global structure information into node embeddings. This model can effectively learn the correlation between the SQL query pattern and its interpretation. Experimental results on the WikiSQL dataset and Stackoverflow dataset show that our model significantly outperforms the Seq2Seq and Tree2Seq baselines, achieving the state-of-the-art performance.