论文标题
SQL查询一代的最新进展：一项调查
Recent Advances in SQL Query Generation: A Survey
论文作者
论文摘要
假设自然语言是许多领域的最佳用户界面。但是，在自然语言和任何其他领域之间提供接口的一般模型仍然不存在。为关系数据库提供自然语言界面可能会吸引绝大多数或不熟悉查询语言的用户。随着深度学习技术的兴起，在为关系数据库设计合适的自然语言界面方面正在进行广泛的研究。 该调查旨在概述自然语言中SQL查询生成领域提出的一些最新方法和模型。我们描述了具有各种体系结构的模型，例如卷积神经网络，经常性神经网络，指针网络，增强学习等。几个旨在解决SQL查询生成问题的数据集得到了解释和简要浏览。最后，在现场使用的评估指标主要是作为执行精度和逻辑形式准确性的组合。
Natural language is hypothetically the best user interface for many domains. However, general models that provide an interface between natural language and any other domain still do not exist. Providing natural language interface to relational databases could possibly attract a vast majority of users that are or are not proficient with query languages. With the rise of deep learning techniques, there is extensive ongoing research in designing a suitable natural language interface to relational databases. This survey aims to overview some of the latest methods and models proposed in the area of SQL query generation from natural language. We describe models with various architectures such as convolutional neural networks, recurrent neural networks, pointer networks, reinforcement learning, etc. Several datasets intended to address the problem of SQL query generation are interpreted and briefly overviewed. In the end, evaluation metrics utilized in the field are presented mainly as a combination of execution accuracy and logical form accuracy.
