Text-to-SQL文本信息处理技术研究综述

A Survey of Text-to-SQL Text Information Processing

信号与信息处理的需求日益增加,离不开数据处理技术,数据处理需要数据库的支持,然而没有经过训练的使用者会因为不熟悉数据库操作产生诸多问题。文本转结构化查询语言(Text to Structured Query Language,Text-to-SQL)的出现,使用户无需掌握结构化查询语言(Structured Query Language,SQL)也能够熟练操作数据库。介绍Text-to-SQL的研究背景及面临的挑战;介绍Text-to-SQL关键技术、基准数据集、模型演变及最新研究进展,关键技术包括Transformer等主流技术,用于模型训练的基准数据集包括WikiSQL和Spider;介绍Text-to-SQL不同阶段模型的特点,详细阐述Text-to-SQL最新研究成果的工作原理,包括模型构建、解析器设计及数据集生成;总结Text-to-SQL未来的发展方向及研究重点。

The demand of signal and information processing grows rapidly and is inseparable for data processing technology,which requires the support of databases.But it will cause many problems due to being unfamiliar with the database operations of untrained users.The emergence of Text to Structured Query Language(Text-to-SQL)makes users do not need to master the Structured Query Language(SQL)to operate databases.Firstly,the research background and challenges of Text-to-SQL techniques are introduced;secondly,the key technologies,benchmark datasets,model developments and the state-of-the-art Text-to-SQL models are introduced;The key technologies include the mainstream techniques including Transformer,and the benchmark datasets used for model training include WikiSQL and Spider,and the characteristics of different stages of Text-to-SQL models are presented,the working mechanism of the state-of-the-art Text-to-SQL models are introduced in detail,including model construction,parser design and dataset generation;Lastly,the future research directions and focus of Text-to-SQL are summarized.