基于自然语言的数据库查询生成研究综述

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.

Survey on Generating Database Queries Based on Natural Language

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.