The Text-to-SQL task, aiming to translate the natural language of the
questions into SQL queries, has drawn much attention recently. One of the most
challenging problems of Text-to-SQL is how to generalize the trained model to
the unseen database schemas, also known as the cross-domain Text-to-SQL task.
The key lies in the generalizability of (i) the encoding method to model the
question and the database schema and (ii) the question-schema linking method to
learn the mapping between words in the question and tables/columns in the
database schema. Focusing on the above two key issues, we propose a
Structure-Aware Dual Graph Aggregation Network (SADGA) for cross-domain
Text-to-SQL. In SADGA, we adopt the graph structure to provide a unified
encoding model for both the natural language question and database schema.
Based on the proposed unified modeling, we further devise a structure-aware
aggregation method to learn the mapping between the question-graph and
schema-graph. The structure-aware aggregation method is featured with Global
Graph Linking, Local Graph Linking, and Dual-Graph Aggregation Mechanism. We
not only study the performance of our proposal empirically but also achieved
3rd place on the challenging Text-to-SQL benchmark Spider at the time of
writing.

本文提出了一种基于图结构编码模型和 Dual-Graph 聚合机制的跨领域 Text-to-SQL 方法，在 Spider Text-to-SQL benchmark 中获得了第三名