| 摘要: |
| 数据库能够提供对大量数据的高效存储和访问，然而查询数据库需要掌握数据库查询语言SQL，对于普通用户而言存在一定的门槛.基于自然语言的数据库查询（即text-to-SQL）在最近几年受到了广泛的关注.对text-to-SQL问题的当前进展进行了系统的分析.首先介绍了问题背景，并对问题进行了描述；其次，重点分析了目前提出的text-to-SQL技术，包括基于流水线的方法、基于统计学习的方法，以及为多轮text-to-SQL而开发的技术，对每种方法都进行了深入的分析和总结.再次，进一步讨论了text-to-SQL所属的语义解析（semantic parsing）这一领域的研究.接着，总结了目前研究中广泛采用的数据集和评价指标，并从多个角度对主流模型进行了比较和分析.最后，总结了text-to-SQL任务面临的挑战，以及未来的研究方向. |
| 关键词: 自然语言 数据库查询 SQL text-to-SQL 语义解析 自然语言处理 |
| DOI：10.13328/j.cnki.jos.006539 |
| 分类号: |
| 基金项目:国家自然科学基金（62076112，61972184，61762042）；江西省自然科学基金（20192BAB207017）；江西省教育厅科技项目（GJJ190255，GJJ180234，GJJ190208）；江西省研究生创新专项（YC2021-B130） |
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| Survey on Generating Database Queries Based on Natural Language |
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LIU Xi-Ping1, SHU Qing1,2, HE Jia-Hao1, WAN Chang-Xuan1, LIU De-Xi1
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1.School of Information Management, Jiangxi University of Finance and Economics, Nanchang 330013, China;2.School of Software, Jiangxi Agricultural University, Nanchang 330013, China
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| Abstract: |
| Database can provide efficient storage and access for massive data. However, it is nontrivial for non-experts to command database query language like SQL, which is essential for querying databases. Hence, querying databases using natural language (i.e., text-to-SQL) has received extensive attention in recent years. This study provides a holistic view of text-to-SQL technologies and elaborates on current advancements. It first introduces the background of the research and describes the research problem. Then the study focuses on the current text-to-SQL technologies, including pipeline-based methods, statistical-learning-based methods, as well as techniques developed for multi-turn text-to-SQL task. The study goes further to discuss the field of semantic parsing to which text-to-SQL belongs. Afterward, it introduces the benchmarks and evaluation metrics that are widely used in the research field. Moreover, it compares and analyzes the state-of-the-art models from multiple perspectives. Finally, the study summarizes the potential challenges for text-to-SQL task, and gives some suggestions for future research. |
| Key words: natural language database query SQL text-to-SQL semantic parsing natural language processing |
