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DuoRAT: Towards Simpler Text-to-SQL Models

Résumé

Recent neural text-to-SQL models can effectively translate natural language questions to corresponding SQL queries on unseen databases. Working mostly on the Spider dataset, researchers have proposed increasingly sophisticated solutions to the problem. Contrary to this trend, in this paper we focus on simplifications. We begin by building DuoRAT, a re-implementation of the state-of-the-art RAT-SQL model that unlike RAT-SQL is using only relation-aware or vanilla transformers as the building blocks. We perform several ablation experiments using DuoRAT as the baseline model. Our experiments confirm the usefulness of some techniques and point out the redundancy of others, including structural SQL features and features that link the question with the schema.

Publication
North American Chapter of the Association for Computational Linguistics (NAACL)
Torsten Scholak
Torsten Scholak
Research Lead

Research Lead at AI Research Deployment​ located at Montreal, QC, Canada.

Raymond Li
Raymond Li
AI Developer

AI Developer at AI Research Deployment​ located at Montreal, QC, Canada.

Christopher Pal
Christopher Pal
Distinguished Scientist

Distinguished Scientist at AI Research Partnerships & Ecosystem​ located at Montreal, QC, Canada.