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The task of text-to-SQL parsing, which aims at converting natural languagequestions into executable SQL queries, has garnered increasing attention inrecent years, as it can assist end users in efficiently extracting vitalinformation from databases without the need for technical background. One ofthe major challenges in text-to-SQL parsing is domain generalization, i.e., howto generalize well to unseen databases. Recently, the pre-trained text-to-texttransformer model, namely T5, though not specialized for text-to-SQL parsing,has achieved state-of-the-art performance on standard benchmarks targetingdomain generalization. In this work, we explore ways to further augment thepre-trained T5 model with specialized components for text-to-SQL parsing. Suchcomponents are expected to introduce structural inductive bias into text-to-SQLparsers thus improving model's capacity on (potentially multi-hop) reasoning,which is critical for generating structure-rich SQLs. To this end, we propose anew architecture GRAPHIX-T5, a mixed model with the standard pre-trainedtransformer model augmented by some specially-designed graph-aware layers.Extensive experiments and analysis demonstrate the effectiveness of GRAPHIX-T5across four text-to-SQL benchmarks: SPIDER, SYN, REALISTIC and DK. GRAPHIX-T5surpass all other T5-based parsers with a significant margin, achieving newstate-of-the-art performance. Notably, GRAPHIX-T5-large reach performancesuperior to the original T5-large by 5.7% on exact match (EM) accuracy and 6.6%on execution accuracy (EX). This even outperforms the T5-3B by 1.2% on EM and1.5% on EX.

Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing