{"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/kaggledbqa-realistic-evaluation-of-text-to","title":"KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers","arxiv_id":"2106.11455","date":"2021-06-22","proceeding":"ACL 2021 5","authors":["Chia-Hsuan Lee","Oleksandr Polozov","Matthew Richardson"],"abstract":"The goal of database question answering is to enable natural language querying of real-life relational databases in diverse application domains. Recently, large-scale datasets such as Spider and WikiSQL facilitated novel modeling techniques for text-to-SQL parsing, improving zero-shot generalization to unseen databases. In this work, we examine the challenges that still prevent these techniques from practical deployment. First, we present KaggleDBQA, a new cross-domain evaluation dataset of real Web databases, with domain-specific data types, original formatting, and unrestricted questions. Second, we re-examine the choice of evaluation tasks for text-to-SQL parsers as applied in real-life settings. Finally, we augment our in-domain evaluation task with database documentation, a naturally occurring source of implicit domain knowledge. We show that KaggleDBQA presents a challenge to state-of-the-art zero-shot parsers but a more realistic evaluation setting and creative use of associated database documentation boosts their accuracy by over 13.2%, doubling their performance.","url_abs":"https://arxiv.org/abs/2106.11455v1","url_pdf":"https://arxiv.org/pdf/2106.11455v1.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":"kaggledbqa-realistic-evaluation-of-text-to","repo_url":"https://github.com/chiahsuan156/KaggleDBQA","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"empty_repo"}},{"paper_slug":"kaggledbqa-realistic-evaluation-of-text-to","repo_url":"https://github.com/saparina/text2sql-nlvariation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"question-answering","task_name":"Question Answering"},{"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"},{"task_slug":"zero-shot-generalization","task_name":"Zero-shot Generalization"}],"methods":[],"datasets_introduced":[{"slug":"kaggledbqa","name":"KaggleDBQA","full_name":"KaggleDBQA: Realistic Text-to-SQL dataset"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-to-sql-on-kaggledbqa","task":"Text-To-SQL","dataset":"KaggleDBQA","model":"RAT-SQL","rank_in_archive_order":1,"of":2,"metrics":{"Exact Match (EM)":"26.77"},"uses_additional_data":false},{"leaderboard":"/sota/text-to-sql-on-kaggledbqa","task":"Text-To-SQL","dataset":"KaggleDBQA","model":"Edit-SQL","rank_in_archive_order":2,"of":2,"metrics":{"Exact Match (EM)":"11.73"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2106.11455","atlas_url":"https://app.syntology.ai/?focus=2106.11455","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}