Querying databases for the right information is a time consuming and error-prone task and often requires experienced professionals for the job. Furthermore, the user needs to have some prior knowledge about the database. There have been various efforts to develop an intelligence which can help business users to query databases directly. However, there has been some successes, but very little in terms of testing and deploying those for real world users. In this paper, we propose a semantic parsing approach to address the challenge of converting complex natural language into SQL and institute a product out of it. For this purpose, we modified state-of-the-art models, by various pre and post processing steps which make the significant part when a model is deployed in production. To make the product serviceable to businesses we added an automatic visualization framework over the queried results.
翻译:为正确信息查询数据库是一项耗时和易出错的任务,往往需要有经验的专业人员来完成这项工作。此外,用户需要事先对数据库有一些知识。已经作出各种努力来开发能够帮助商业用户直接查询数据库的情报。然而,在测试和为现实世界用户部署这些情报方面,取得了一些成功,但在测试和为现实世界用户部署这些情报方面却很少。在本文件中,我们建议采用语义分解方法来应对将复杂的自然语言转换成SQL的挑战,并从中建立一种产品。为此目的,我们通过各种前期和后期处理步骤,修改了最先进的模型,使模型在生产时成为重要部分。为了使产品对企业有用,我们增加了一个自动可视化框架来应对所询问的结果。