Natural language to SQL (NL2SQL) aims to parse a natural language with a
given database into a SQL query, which widely appears in practical Internet
applications. Jointly encode database schema and question utterance is a
difficult but important task in NL2SQL. One solution is to treat the input as a
heterogeneous graph. However, it failed to learn good word representation in
question utterance. Learning better word representation is important for
constructing a well-designed NL2SQL system. To solve the challenging task, we
present a Relation aware Semi-autogressive Semantic Parsing (\MODN) ~framework,
which is more adaptable for NL2SQL. It first learns relation embedding over the
schema entities and question words with predefined schema relations with
ELECTRA and relation aware transformer layer as backbone. Then we decode the
query SQL with a semi-autoregressive parser and predefined SQL syntax. From
empirical results and case study, our model shows its effectiveness in learning
better word representation in NL2SQL.

该论文提出了一种基于关系嵌入的半自回归语义分析 NL2SQL 方法，通过 ELECTRA 和关系感知 transformer 层实现与模式实体及问题单词之间的关系嵌入，再使用半自回归解析器和预定义的 SQL 语法解码查询 SQL，从实验结果和案例研究来看，该方法在 NL2SQL 中具有更好的词汇表现力。