---
title: "SParC: Cross-Domain Semantic Parsing in Context — Yu 2019"
description: "Most text-to-SQL research treats each question in isolation."
authors: ["Tao Yu", "Rui Zhang", "Michihiro Yasunaga", "Yi Chern Tan", "Xi Victoria Lin", "Suyi Li", "Heyang Er", "Irene Li", "Bo Pang", "Tao Chen", "Emily Ji", "Shreya Dixit", "David Proctor", "Sungrok Shim", "Jonathan Kraft", "Vincent Zhang", "Caiming Xiong", "Richard Socher", "Dragomir Radev"]
year: 2019
venue: "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics"
source: "https://aclanthology.org/P19-1443/"
doi: "10.18653/v1/p19-1443"
arxiv: "1906.02285"
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/yu-2019-sparc-cross-domain-semantic-parsing/"
---
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# SParC: Cross-Domain Semantic Parsing in Context

**Authors:** [Tao Yu](https://md.research.elamur.ai/free/en/authors/tao-yu/), [Rui Zhang](https://md.research.elamur.ai/free/en/authors/rui-zhang/), [Michihiro Yasunaga](https://md.research.elamur.ai/free/en/authors/michihiro-yasunaga/), [Yi Chern Tan](https://md.research.elamur.ai/free/en/authors/yi-chern-tan/), [Xi Victoria Lin](https://md.research.elamur.ai/free/en/authors/xi-victoria-lin/), [Suyi Li](https://md.research.elamur.ai/free/en/authors/suyi-li/), [Heyang Er](https://md.research.elamur.ai/free/en/authors/heyang-er/), [Irene Li](https://md.research.elamur.ai/free/en/authors/irene-li/), [Bo Pang](https://md.research.elamur.ai/free/en/authors/bo-pang/), Tao Chen, Emily Ji, Shreya Dixit et al. (19 authors) · **Year:** 2019 · **Venue:** Proceedings of the 57th Annual Meeting of the 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/P19-1443/) · [PDF](https://aclanthology.org/P19-1443.pdf) · [DOI 10.18653/v1/p19-1443](https://doi.org/10.18653/v1/p19-1443) · [arXiv 1906.02285](https://arxiv.org/abs/1906.02285) · [OpenAlex](https://openalex.org/W2948465491)

**Citations:** 141 citations (openalex-snapshot · 2026-10-14) · [86](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Most text-to-SQL research treats each question in isolation. Real users, however, tend to ask chains of related questions, where later turns leave out, point back to, or adjust earlier constraints. The main existing benchmark for this setting, ATIS, covers only flight booking with a single database. The authors fill this gap with SParC, an expert-annotated corpus of context-dependent question sequences built on top of the Spider databases. Fifteen students with SQL experience wrote the data. Each student took a Spider question as the interaction goal, asked a sequence of linked questions working toward it, and then labeled every question with SQL. Databases are split so that test-time schemas are unseen during training. The authors analyze contextual and thematic phenomena in the data. They also test two context-aware adaptations of existing text-to-SQL parsers. Both perform poorly, and accuracy collapses in later turns. SParC therefore offers a hard, cross-domain testbed for conversational interfaces to databases.

## Key points
- SParC contains 4,298 question sequences with over 12k SQL-annotated questions, spanning 200 databases across 138 domains. It is derived from Spider, and train, dev and test sets share no databases.
- The baselines are CD-Seq2Seq, which uses turn-level history encoders, and SyntaxSQL-con, a SyntaxSQLNet variant conditioned on the previous question.
- The best exact set match accuracy is 20.2% over all questions and below 10% over full interactions.
- On the dev set, SyntaxSQL-con reaches 38.6% on first-turn questions but only 1.1% on turn 4 and later. Accuracy also falls sharply on hard and extra hard SQL.
- The data, baselines and leaderboard are available at yale-lily.github.io/sparc.

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

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

86 papers of our library cite it (of the 140884 whose bibliographies we read); 6 of them have a page here, 80 not yet.

- [Ar-Spider: Text-to-SQL in Arabic](https://md.research.elamur.ai/free/en/papers/almohaimeed-2024-ar-spider-text-sql-arabic/)
- [Prompt Engineering Techniques for Context-dependent Text-to-SQL in Arabic](https://md.research.elamur.ai/free/en/papers/almohaimeed-2025-prompt-engineering-techniques-context-dependent/)
- [Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing](https://md.research.elamur.ai/free/en/papers/lin-2020-bridging-textual-tabular-data-cross/)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
- [UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models](https://md.research.elamur.ai/free/en/papers/xie-2022-unifiedskg-unifying-multi-tasking-structured/)
- [CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases](https://md.research.elamur.ai/free/en/papers/yu-2019-cosql-conversational-text-sql-challenge/)

## Cite

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

```bibtex
@inproceedings{yu2019sparc,
  title = {{SParC: Cross-Domain Semantic Parsing in Context}},
  author = {Yu, Tao and Zhang, Rui and Yasunaga, Michihiro and Tan, Yi Chern and Lin, Xi Victoria and Li, Suyi and Er, Heyang and Li, Irene and Pang, Bo and Chen, Tao and Ji, Emily and Dixit, Shreya and Proctor, David and Shim, Sungrok and Kraft, Jonathan and Zhang, Vincent and Xiong, Caiming and Socher, Richard and Radev, Dragomir},
  booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
  year = {2019},
  doi = {10.18653/v1/p19-1443},
  eprint = {1906.02285},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/p19-1443}
}
```

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