论文标题
s $^2 $ sql：将语法注入Question-Schema交互图形编码器，用于文本到SQL解析器
S$^2$SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers
论文作者
论文摘要
将自然语言问题转换为可执行的SQL查询（称为文本到SQL）的任务是语义解析的重要分支。基于图形的最先进的编码器已成功地用于此任务中，但并不能很好地模拟问题语法。在本文中，我们提出了S $^2 $ SQL，将语法注入了Question-Schema图编码器的文本到SQL Parsers，这些编码器有效地利用了文本到SQL中问题的语法依赖性信息来提高性能。我们还采用了脱钩约束来诱导各种关系边缘嵌入，从而进一步改善了网络的性能。蜘蛛和鲁棒性设置蜘蛛合的实验表明，当使用预训练模型时，所提出的方法优于所有现有方法，从而在蜘蛛排行榜上首先排名在蜘蛛排行榜上排名。
The task of converting a natural language question into an executable SQL query, known as text-to-SQL, is an important branch of semantic parsing. The state-of-the-art graph-based encoder has been successfully used in this task but does not model the question syntax well. In this paper, we propose S$^2$SQL, injecting Syntax to question-Schema graph encoder for Text-to-SQL parsers, which effectively leverages the syntactic dependency information of questions in text-to-SQL to improve the performance. We also employ the decoupling constraint to induce diverse relational edge embedding, which further improves the network's performance. Experiments on the Spider and robustness setting Spider-Syn demonstrate that the proposed approach outperforms all existing methods when pre-training models are used, resulting in a performance ranks first on the Spider leaderboard.
