SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL

• URL: http://arxiv.org/abs/2111.00653v1
• Date: Mon, 1 Nov 2021 01:50:28 GMT
• Title: SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL
• Authors: Ruichu Cai, Jinjie Yuan, Boyan Xu, Zhifeng Hao
• Abstract summary: One of the most challenging problems of Text-to-Graph is how to generalize the trained model to the unseen database schemas. We propose a Structure-Aware Dual Graph Aggregation Network (SADGA) for cross-domain Text-to-Graph. We achieve 3rd place on the challenging Text-to-Graph benchmark Spider at the time of writing.
• Score: 29.328698264910596
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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