Faster and Better Grammar-Based Text-to-SQL Parsing via Clause-Level Parallel Decoding and Alignment Loss

Kun Wu, Lijie Wang, Zhenghua Li, Xinyan Xiao

Abstract: As a mainstream approach, grammar-based models have achieved high performance in text-to-SQL parsing task, but suffer from low decoding efficiency since the number of actions for building SQL trees are much larger than the number of tokens in SQL queries. Meanwhile, intuitively it is beneficial from the parsing performance perspective to incorporate alignment information between SQL clauses and question segments. This paper proposes clause-level parallel decoding and alignment loss to enhance two high-performance grammar-based parsers, i.e., RATSQL and LGESQL. Experiments on the Spider dataset show our approach improves the decoding speed of RATSQL and LGESQL by 18.9% and 35.5% respectively, and also achieves consistent improvement in parsing accuracy, especially on complex questions.