论文标题
RyansQL：递归应用基于草图的插槽填充物，以在跨域数据库中进行复杂的文本到SQL
RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databases
论文作者
论文摘要
在给出问题和数据库时，文本到SQL是将用户问题转换为SQL查询的问题。在本文中，我们提出了一种称为ryansql的神经网络方法（递归产生SQL的注释网络），以解决跨域数据库的复杂文本到SQL任务。国会位置代码（SPC）被定义为将嵌套的SQL查询转移到一组非嵌套的选择语句中；提出了一种基于草图的插槽填充方法，以合成其相应的SPC的每个选择语句。此外，提出了两种输入操作方法，以进一步提高发电性能。 Ryansql在具有挑战性的蜘蛛基准上实现了58.2％的精度，比以前的最新方法相比，P提高了3.2％。在写作时，Ryansql在蜘蛛排行榜上获得了第一个位置。
Text-to-SQL is the problem of converting a user question into an SQL query, when the question and database are given. In this paper, we present a neural network approach called RYANSQL (Recursively Yielding Annotation Network for SQL) to solve complex Text-to-SQL tasks for cross-domain databases. State-ment Position Code (SPC) is defined to trans-form a nested SQL query into a set of non-nested SELECT statements; a sketch-based slot filling approach is proposed to synthesize each SELECT statement for its corresponding SPC. Additionally, two input manipulation methods are presented to improve generation performance further. RYANSQL achieved 58.2% accuracy on the challenging Spider benchmark, which is a 3.2%p improvement over previous state-of-the-art approaches. At the time of writing, RYANSQL achieves the first position on the Spider leaderboard.
