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Grounding Natural Language to SQL Translation with Data-Based Self-Explanations by Fan, Yuankai; Ren, Tonghui; Huang, Can; He, Zhenying; Wang, X. Sean is a scholarly article available to read on EtoBox.

What is Grounding Natural Language to SQL Translation with Data-Based Self-Explanations about?

Natural Language Interfaces for Databases empower non-technical users to interact with data using natural language (NL). Advanced approaches, utilizing either neural sequence-to-sequence or more recent sophisticated large-scale language models, typically implement NL to SQL (NL2SQL) translation in an end-to-end fashion. However, like humans, these end-to-end translation models may not always generate the best SQL output on their first try. In this paper, we propose CycleSQL, an iterative framework designed for end-to-end translation models to autonomously generate the best output through self-evaluation. The main idea of CycleSQL is to introduce data-grounded NL explanations of query results as self-provided feedback, and use the feedback to validate the correctness of the translation iteratively, hence improving the overall translation accuracy. Extensive experiments, including quantitative and qualitative evaluations, are conducted to study CycleSQL by applying it to seven existing translation models on five widely used benchmarks. The results show that 1) the feedback loop introduced in CycleSQL can consistently improve the performance of existing models, and in particular, by a

Author

Fan, Yuankai; Ren, Tonghui; Huang, Can; He, Zhenying; Wang, X. Sean

Published

2024

Language

EN