{"url":"/sota/text-to-sql-on-sparc","task":{"name":"Text-To-SQL","url":"/task/text-to-sql","note":null},"dataset":{"name":"SParC","url":"/dataset/sparc"},"category":"Natural Language Processing","categories":["Computer Code","Natural Language Processing"],"category_note":null,"description":"**Text-to-SQL** is a task in natural language processing (NLP) where the goal is to automatically generate SQL queries from natural language text. The task involves converting the text input into a structured representation and then using this representation to generate a semantically correct SQL query that can be executed on a database.\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [SyntaxSQLNet](https://arxiv.org/pdf/1810.05237v2.pdf) )</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["interaction match accuracy","question match accuracy"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"interaction match accuracy":"higher","question match accuracy":"higher"}},"counts":{"rows":7,"rows_with_code":5,"rows_with_paper_page":7,"rows_dated":7,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"RASAT+PICARD","metrics":{"interaction match accuracy":"45.2","question match accuracy":"67.7"},"uses_additional_data":false,"paper_date":"2022-05-14","paper":"/paper/rasat-integrating-relational-structures-into","paper_url":"https://arxiv.org/abs/2205.06983v2","paper_title":"RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL","code":"https://github.com/lumia-group/rasat","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":9,"n_samples":9,"n_pointer_only_licence":0}},{"rank_in_archive_order":2,"model":"RAT-SQL-TC + GAP","metrics":{"interaction match accuracy":"43.2","question match accuracy":"65.7"},"uses_additional_data":false,"paper_date":"2021-12-16","paper":"/paper/pay-more-attention-to-history-a-context","paper_url":"https://arxiv.org/abs/2112.08735v2","paper_title":"Pay More Attention to History: A Context Modelling Strategy for Conversational Text-to-SQL","code":"https://github.com/juruomp/rat-sql-tc","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"HIE-SQL + GraPPa","metrics":{"interaction match accuracy":"42.9","question match accuracy":"64.6"},"uses_additional_data":false,"paper_date":"2021-11-16","paper":"/paper/hie-sql-history-information-enhanced-network","paper_url":"https://openreview.net/forum?id=fX8TXF-LD21","paper_title":"HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsing","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":4,"model":"RAT-SQL + SCoRe","metrics":{"interaction match accuracy":"38.1","question match accuracy":"62.4"},"uses_additional_data":false,"paper_date":"2021-01-01","paper":"/paper/score-pre-training-for-context-representation","paper_url":"https://openreview.net/forum?id=oyZxhRI2RiE","paper_title":"SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":5,"model":"EditSQL + BERT","metrics":{"interaction match accuracy":"25.3","question match accuracy":"47.9"},"uses_additional_data":false,"paper_date":"2019-09-02","paper":"/paper/editing-based-sql-query-generation-for-cross","paper_url":"https://arxiv.org/abs/1909.00786v2","paper_title":"Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions","code":"https://github.com/ryanzhumich/editsql","n_code_links":3,"syntology":null},{"rank_in_archive_order":6,"model":"GAZP + BERT","metrics":{"interaction match accuracy":"23.5","question match accuracy":"45.9"},"uses_additional_data":false,"paper_date":"2020-09-16","paper":"/paper/grounded-adaptation-for-zero-shot-executable","paper_url":"https://arxiv.org/abs/2009.07396v3","paper_title":"Grounded Adaptation for Zero-shot Executable Semantic Parsing","code":"https://github.com/vzhong/gazp","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":11,"n_samples":11,"n_pointer_only_licence":0}},{"rank_in_archive_order":7,"model":"SyntaxSQL-con","metrics":{"interaction match accuracy":"5.2","question match accuracy":"20.2"},"uses_additional_data":false,"paper_date":"2018-10-11","paper":"/paper/syntaxsqlnet-syntax-tree-networks-for-complex","paper_url":"http://arxiv.org/abs/1810.05237v2","paper_title":"SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task","code":"https://github.com/taoyds/syntaxsql","n_code_links":2,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":2,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":2,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":20,"n_samples":20,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":20,"n_samples":20,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}