---
title: "LGESQL — paper by Cao et al. (2021) — summary and ideas"
description: "Text-to-SQL systems must jointly encode a natural-language question and a database schema of… 60 references, cited by 53 papers here."
authors: ["Ruisheng Cao", "Lu Chen", "Zhi Chen", "Yanbin Zhao", "Su Zhu", "Kai Yu"]
year: 2021
venue: "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)"
source: "https://aclanthology.org/2021.acl-long.198/"
doi: "10.18653/v1/2021.acl-long.198"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/cao-2021-lgesql-line-graph-enhanced-text/"
---
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# LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations

**Authors:** [Ruisheng Cao](https://md.research.elamur.ai/free/en/authors/ruisheng-cao/), [Lu Chen](https://md.research.elamur.ai/free/en/authors/lu-chen/), [Zhi Chen](https://md.research.elamur.ai/free/en/authors/zhi-chen/), [Yanbin Zhao](https://md.research.elamur.ai/free/en/authors/yanbin-zhao/), Su Zhu and [Kai Yu](https://md.research.elamur.ai/free/en/authors/kai-yu/) · **Year:** 2021 · **Venue:** [Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2021](https://md.research.elamur.ai/free/en/venues/acl/2021/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.acl-long.198/) · [PDF](https://aclanthology.org/2021.acl-long.198.pdf) · [DOI 10.18653/v1/2021.acl-long.198](https://doi.org/10.18653/v1/2021.acl-long.198) · [OpenAlex](https://openalex.org/W3175488485)

**Citations:** 138 citations (OpenAlex, 2026-10-14) · [53](https://md.research.elamur.ai/free/en/papers/cao-2021-lgesql-line-graph-enhanced-text/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Text-to-SQL systems must jointly encode a natural-language question and a database schema of tables and columns, which form a heterogeneous graph. Earlier encoders such as GNNSQL and RATSQL take each edge type from a fixed embedding table. As a result, they ignore how edges link to one another, and they treat direct (1-hop) and multi-hop relations the same way, which risks over-smoothing.

LGESQL addresses this with a line graph, in which every local edge of the original question–schema graph becomes a node. Stacked Dual RGAT layers update both graphs together. Line-graph node states act as dynamic features for local edges, while non-local relations keep static learned embeddings. Node inputs come from GloVe or a pre-trained language model, followed by type-specific BiLSTMs. An auxiliary objective called graph pruning trains the encoder to identify which schema items are relevant to the question.

On the cross-domain Spider benchmark, the model reached state-of-the-art exact set match accuracy at the time of writing, both with and without pre-trained encoders. This shows that explicitly modeling edge topology benefits schema-aware semantic parsing.

## Key points
- Spider exact set match accuracy: 62.8% with GloVe and 72.0% with ELECTRA, both state of the art when the paper was written.
- The line graph is built only from local relations, and reverse edges are not connected to each other (no backtracking). Useful meta-paths emerge through iteration, so they do not have to be hand-designed or searched for.
- The graph pruning auxiliary task has the encoder classify each schema node as relevant or irrelevant to the question.
- In the PLM ablation (Table 5), LGESQL beats the RGATSQL baseline with every encoder: bert-base 70.5→71.4, electra-base 72.8→73.4, bert-large 72.3→73.5, grappa-large 73.1→74.0, and electra-large 74.8→75.1. The authors link ELECTRA's advantage to its replaced-token detection objective, which resembles graph pruning.
- Case studies show gains on queries that join multiple tables. A noted failure case adds an unneeded table ("airports").

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Shanghai Jiao Tong University](https://md.research.elamur.ai/free/en/universities/shanghai-jiao-tong-university/) — University, Shanghai, China · 63 works · 15,047 citations

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## Cite

Cite as: Ruisheng Cao, Lu Chen, Zhi Chen, Yanbin Zhao, Su Zhu and Kai Yu. 2021. LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{cao2021lgesql,
  title = {{LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations}},
  author = {Ruisheng Cao, Lu Chen, Zhi Chen, Yanbin Zhao, Su Zhu and Kai Yu},
  booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  year = {2021},
  doi = {10.18653/v1/2021.acl-long.198},
  url = {https://doi.org/10.18653/v1/2021.acl-long.198}
}
```

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*Updated 24 October 2026*