Relational databases play an important role in this Big Data era. However, it is challenging for non-experts to fully unleash the analytical power of relational databases, since they are not familiar with database languages such as SQL. Many techniques have been proposed to automatically generate SQL from natural language, but they suffer from two issues: (1) they still make many mistakes, particularly for complex queries, and (2) they do not provide a flexible way for non-expert users to validate and refine the incorrect queries. To address these issues, we introduce a new interaction mechanism that allows users directly edit a step-by-step explanation of an incorrect SQL to fix SQL errors. Experiments on the Spider benchmark show that our approach outperforms three SOTA approaches by at least 31.6% in terms of execution accuracy. A user study with 24 participants further shows that our approach helped users solve significantly more SQL tasks with less time and higher confidence, demonstrating its potential to expand access to databases, particularly for non-experts.

翻译：关系数据库在大数据时代扮演着重要角色。然而，非专业用户难以充分释放关系数据库的分析能力，因为他们不熟悉SQL等数据库语言。虽然已有多种技术被提出用于从自然语言自动生成SQL，但这些方法存在两个问题：(1) 仍会产生大量错误，尤其是在处理复杂查询时；(2) 未能为非专业用户提供灵活的方式来验证和修正错误查询。针对上述问题，我们提出了一种新型交互机制，允许用户通过直接编辑错误SQL的逐步解释来修正查询错误。在Spider基准上的实验表明，我们的方法在执行准确率上比三种最先进方法至少提升31.6%。包含24名参与者的用户研究进一步证明，该方法能帮助用户用更短时间、更高置信度解决显著更多的SQL任务，展现出拓展数据库访问权限的潜力，尤其对非专业用户而言。