---
title: "Editing-Based SQL Query Generation for Cross-Domain"
description: "The paper tackles text-to-SQL generation in multi-turn conversations, where follow-up questions… 55 references, cited by 60 papers here."
authors: ["Rui Zhang", "Tao Yu", "Heyang Er", "Sungrok Shim", "Eric Xue", "Xi Victoria Lin", "Tianze Shi", "Caiming Xiong", "Richard Socher", "Dragomir Radev"]
year: 2019
venue: "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)"
source: "https://aclanthology.org/D19-1537/"
doi: "10.18653/v1/d19-1537"
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/zhang-2019-editing-based-sql-query-generation/"
---
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# Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions

**Authors:** [Rui Zhang](https://md.research.elamur.ai/free/en/authors/rui-zhang/), [Tao Yu](https://md.research.elamur.ai/free/en/authors/tao-yu/), [Heyang Er](https://md.research.elamur.ai/free/en/authors/heyang-er/), Sungrok Shim, [Eric Xue](https://md.research.elamur.ai/free/en/authors/eric-xue/), [Xi Victoria Lin](https://md.research.elamur.ai/free/en/authors/xi-victoria-lin/), [Tianze Shi](https://md.research.elamur.ai/free/en/authors/tianze-shi/), [Caiming Xiong](https://md.research.elamur.ai/free/en/authors/caiming-xiong/), [Richard Socher](https://md.research.elamur.ai/free/en/authors/richard-socher/) and [Dragomir Radev](https://md.research.elamur.ai/free/en/authors/dragomir-radev/) · **Year:** 2019 · **Venue:** [Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2019](https://md.research.elamur.ai/free/en/venues/emnlp/2019/)) · **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/D19-1537/) · [PDF](https://aclanthology.org/D19-1537.pdf) · [DOI 10.18653/v1/d19-1537](https://doi.org/10.18653/v1/d19-1537) · [OpenAlex](https://openalex.org/W2970172141)

**Citations:** 115 citations (OpenAlex, 2026-10-14) · [60](https://md.research.elamur.ai/free/en/papers/zhang-2019-editing-based-sql-query-generation/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper tackles text-to-SQL generation in multi-turn conversations, where follow-up questions depend on earlier turns and the database changes from one interaction to the next. The authors observe that consecutive SQL queries in a dialogue often share much of their structure. They therefore generate each new query by editing the one predicted in the previous turn. The previous query is treated as a token sequence, and at every decoding step a switch chooses between reusing one of its tokens and producing a new one.

Unlike copying whole rule-extracted segments, this token-level editing can make small changes and suffers less from accumulated errors. To handle unseen schemas, the model pairs a BERT-based utterance-table encoder, which uses co-attention between questions and column headers, with turn attention over earlier utterances and a table-aware decoder.

On SParC, editing substantially improves both question-level and interaction-level accuracy. The gains grow as the underlying single-turn parser becomes stronger, which makes the approach a simple and transferable addition to conversational database interfaces.

## Key points
- Main benchmark is SParC: 4,298 interactions and 12,726 questions over 200 databases in 138 domains. The model is also evaluated on Spider (context-independent) and ATIS (single domain).
- The best model, combining BERT embeddings, query attention and editing, reaches 47.2% question match and 29.5% interaction match on the SParC dev set. With BERT, editing adds roughly 7 points (question) and 11 points (interaction).
- The ablation shows that query attention alone yields 42.7% and 21.6%, so attention and editing each contribute separately.
- The segment-copy approach of Suhr et al. (2018) proved sensitive to error propagation on SParC.
- Results on other datasets: on Spider, the BERT version scores 57.6% dev and 53.4% test, comparable to IRNet with BERT. On ATIS, it scores 36.2% dev and 43.9% test, on par with Suhr et al. (2018).

*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

- [Cornell University](https://md.research.elamur.ai/free/en/universities/cornell-university/) — University, Ithaca, United States · 89 works · 58,422 citations
- [Yale University](https://md.research.elamur.ai/free/en/universities/yale-university/) — University, New Haven, United States · 54 works · 11,964 citations

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## Cite

Cite as: Rui Zhang, Tao Yu, Heyang Er, Sungrok Shim, Eric Xue, Xi Victoria Lin, Tianze Shi, Caiming Xiong, Richard Socher and Dragomir Radev. 2019. Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP).

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

```bibtex
@inproceedings{zhang2019editing,
  title = {{Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions}},
  author = {Rui Zhang, Tao Yu, Heyang Er, Sungrok Shim, Eric Xue, Xi Victoria Lin, Tianze Shi, Caiming Xiong, Richard Socher and Dragomir Radev},
  booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
  year = {2019},
  doi = {10.18653/v1/d19-1537},
  url = {https://doi.org/10.18653/v1/d19-1537}
}
```

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*Updated 24 October 2026*