{"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/bidirectional-attention-for-sql-generation","title":"Bidirectional Attention for SQL Generation","arxiv_id":"1801.00076","date":"2017-12-30","proceeding":null,"authors":["Tong Guo","Huilin Gao"],"abstract":"Generating structural query language (SQL) queries from natural language is a\nlong-standing open problem. Answering a natural language question about a\ndatabase table requires modeling complex interactions between the columns of\nthe table and the question. In this paper, we apply the synthesizing approach\nto solve this problem. Based on the structure of SQL queries, we break down the\nmodel to three sub-modules and design specific deep neural networks for each of\nthem. Taking inspiration from the similar machine reading task, we employ the\nbidirectional attention mechanisms and character-level embedding with\nconvolutional neural networks (CNNs) to improve the result. Experimental\nevaluations show that our model achieves the state-of-the-art results in\nWikiSQL dataset.","url_abs":"http://arxiv.org/abs/1801.00076v6","url_pdf":"http://arxiv.org/pdf/1801.00076v6.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":"bidirectional-attention-for-sql-generation","repo_url":"https://github.com/guotong1988/NL2SQL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"bidirectional-attention-for-sql-generation","repo_url":"https://github.com/openbotai/nl2sql","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"gone","observed_at":"2026-09-17","how":"tree_404+repo_404"}}],"tasks":[{"task_slug":"code-generation","task_name":"Code Generation"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/code-generation-on-wikisql","task":"Code Generation","dataset":"WikiSQL","model":"Bidirectional Attention for SQL Generation","rank_in_archive_order":8,"of":10,"metrics":{"Exact Match Accuracy":"69","Execution Accuracy":"62.5"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1801.00076","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}