In Text-to-SQL semantic parsing, selecting the correct entities (tables and
columns) for the generated SQL query is both crucial and challenging; the
parser is required to connect the natural language (NL) question and the SQL
query to the structured knowledge in the database. We formulate two linking
processes to address this challenge: schema linking which links explicit NL
mentions to the database and structural linking which links the entities in the
output SQL with their structural relationships in the database schema.
Intuitively, the effectiveness of these two linking processes changes based on
the entity being generated, thus we propose to dynamically choose between them
using a gating mechanism. Integrating the proposed method with two graph neural
network-based semantic parsers together with BERT representations demonstrates
substantial gains in parsing accuracy on the challenging Spider dataset.
Analyses show that our proposed method helps to enhance the structure of the
model output when generating complicated SQL queries and offers more
explainable predictions.

提出了一种使用门控机制动态选择连接过程、将两种连接过程集成到两个基于图神经网络的语义解析器中，并与 BERT 表征一起演示在具有挑战性的 Spider 数据集上的显著性能提升，该方法有助于增强模型输出的结构并提供更可解释的预测。