{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sparc-cross-domain-semantic-parsing-in","title":"SParC: Cross-Domain Semantic Parsing in Context","arxiv_id":"1906.02285","date":"2019-06-05","proceeding":"ACL 2019 7","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"],"abstract":"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.","url_abs":"https://arxiv.org/abs/1906.02285v1","url_pdf":"https://arxiv.org/pdf/1906.02285v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sparc-cross-domain-semantic-parsing-in","repo_url":"https://github.com/ryanzhumich/editsql","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"sparc-cross-domain-semantic-parsing-in","repo_url":"https://github.com/amolk/editsql","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"sparc-cross-domain-semantic-parsing-in","repo_url":"https://github.com/ryanzhumich/sparc_atis_pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"sparc-cross-domain-semantic-parsing-in","repo_url":"https://github.com/taoyds/sparc","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"semantic-parsing","task_name":"Semantic Parsing"},{"task_slug":"text-to-sql","task_name":"Text to SQL"},{"task_slug":"text-to-sql","task_name":"Text-To-SQL"}],"methods":[],"datasets_introduced":[{"slug":"sparc","name":"SParC","full_name":"Semantic Parsing in Context"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1906.02285","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.02285"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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