{"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/a-transfer-learnable-natural-language","title":"A Transfer-Learnable Natural Language Interface for Databases","arxiv_id":"1809.02649","date":"2018-09-07","proceeding":null,"authors":["Wenlu Wang","Yingtao Tian","Hongyu Xiong","Haixun Wang","Wei-Shinn Ku"],"abstract":"Relational database management systems (RDBMSs) are powerful because they are\nable to optimize and answer queries against any relational database. A natural\nlanguage interface (NLI) for a database, on the other hand, is tailored to\nsupport that specific database. In this work, we introduce a general purpose\ntransfer-learnable NLI with the goal of learning one model that can be used as\nNLI for any relational database. We adopt the data management principle of\nseparating data and its schema, but with the additional support for the\nidiosyncrasy and complexity of natural languages. Specifically, we introduce an\nautomatic annotation mechanism that separates the schema and the data, where\nthe schema also covers knowledge about natural language. Furthermore, we\npropose a customized sequence model that translates annotated natural language\nqueries to SQL statements. We show in experiments that our approach outperforms\nprevious NLI methods on the WikiSQL dataset and the model we learned can be\napplied to another benchmark dataset OVERNIGHT without retraining.","url_abs":"http://arxiv.org/abs/1809.02649v1","url_pdf":"http://arxiv.org/pdf/1809.02649v1.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":"a-transfer-learnable-natural-language","repo_url":"https://github.com/VV123/NLIDB_gradient","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"a-transfer-learnable-natural-language","repo_url":"https://github.com/VV123/SpatialNLI","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"management","task_name":"Management"},{"task_slug":"natural-language-queries","task_name":"Natural Language Queries"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.02649","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}