{"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/improving-text-to-sql-evaluation-methodology","title":"Improving Text-to-SQL Evaluation Methodology","arxiv_id":"1806.09029","date":"2018-06-23","proceeding":"ACL 2018 7","authors":["Catherine Finegan-Dollak","Jonathan K. Kummerfeld","Li Zhang","Karthik Ramanathan","Sesh Sadasivam","Rui Zhang","Dragomir Radev"],"abstract":"To be informative, an evaluation must measure how well systems generalize to\nrealistic unseen data. We identify limitations of and propose improvements to\ncurrent evaluations of text-to-SQL systems. First, we compare human-generated\nand automatically generated questions, characterizing properties of queries\nnecessary for real-world applications. To facilitate evaluation on multiple\ndatasets, we release standardized and improved versions of seven existing\ndatasets and one new text-to-SQL dataset. Second, we show that the current\ndivision of data into training and test sets measures robustness to variations\nin the way questions are asked, but only partially tests how well systems\ngeneralize to new queries; therefore, we propose a complementary dataset split\nfor evaluation of future work. Finally, we demonstrate how the common practice\nof anonymizing variables during evaluation removes an important challenge of\nthe task. Our observations highlight key difficulties, and our methodology\nenables effective measurement of future development.","url_abs":"http://arxiv.org/abs/1806.09029v1","url_pdf":"http://arxiv.org/pdf/1806.09029v1.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":"improving-text-to-sql-evaluation-methodology","repo_url":"https://github.com/jkkummerfeld/text2sql-data","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"sql-parsing","task_name":"SQL Parsing"},{"task_slug":"text-to-sql","task_name":"Text to SQL"},{"task_slug":"text-to-sql","task_name":"Text-To-SQL"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.09029","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.09029"}},"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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