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SParC: Cross-Domain Semantic Parsing in Context

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arxiv 1906.02285 v1 pith:JFNHNH34 submitted 2019-06-05 cs.CL cs.AI

Tao Yu , Rui Zhang , Michihiro Yasunaga , Yi Chern Tan , Xi Victoria Lin , Suyi Li , Heyang Er , Irene Li
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Bo Pang Tao Chen Emily Ji Shreya Dixit David Proctor Sungrok Shim Jonathan Kraft Vincent Zhang Caiming Xiong Richard Socher Dragomir Radev
This is my paper · ORCID
classification cs.CLcs.AI
keywords sparccross-domaindatasetchallengescomplexdatabasesdomainsquestions
verification ladder T0 review T1 audit T2 compute T3 formal
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We present SParC, a dataset for cross-domainSemanticParsing inContext that consists of 4,298 coherent question sequences (12k+ individual questions annotated with SQL queries). It is obtained from controlled user interactions with 200 complex databases over 138 domains. We provide an in-depth analysis of SParC and show that it introduces new challenges compared to existing datasets. SParC demonstrates complex contextual dependencies, (2) has greater semantic diversity, and (3) requires generalization to unseen domains due to its cross-domain nature and the unseen databases at test time. We experiment with two state-of-the-art text-to-SQL models adapted to the context-dependent, cross-domain setup. The best model obtains an exact match accuracy of 20.2% over all questions and less than10% over all interaction sequences, indicating that the cross-domain setting and the con-textual phenomena of the dataset present significant challenges for future research. The dataset, baselines, and leaderboard are released at https://yale-lily.github.io/sparc.

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Cited by 2 Pith papers

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  1. SANE Schema-aware Natural-language Evaluation of Biological Data

    cs.CL 2026-06 unverdicted novelty 6.0 of 10

    SANE is a new schema-aware benchmark paradigm for text-to-SQL evaluation that demonstrates few-shot LLMs with structured prompting can generate accurate queries on constrained biological data schemas without fine-tuning.

  2. Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types

    cs.CL 2024-12 conditional novelty 6.0 of 10

    MMSQL is a multi-turn text-to-SQL benchmark with four question types, and a multi-agent framework with a Question Detector improves LLM performance on it.

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