A Question-Aware Few-Shot Text-to-SQL Neural Model for Industrial Databases

Abstract

Intelligent question answering over industrial databases is a challenging task due to the multicolumn context and complex questions. The existing methods need to be improved in terms of SQL generation accuracy. In this paper, we propose a question-aware few-shot Text-to-SQL approach based on the SDCUP pretrained model. Specifically, an attention-based filtering approach is proposed to reduce the redundant information from multiple columns in the industrial database scenario. We further propose an operator semantics enhancement method to improve the ability of identifying complex conditions in queries. Experimental results on the industrial benchmarks in the fields of electric energy and structural inspection show that the proposed model outperforms the baseline models across all few-shot settings.