Translating natural language utterances to executable queries is a helpful
technique in making the vast amount of data stored in relational databases
accessible to a wider range of non-tech-savvy end users. Prior work in this
area has largely focused on textual input that is linguistically correct and
semantically unambiguous. However, real-world user queries are often succinct,
colloquial, and noisy, resembling the input of a search engine. In this work,
we introduce data augmentation techniques and a sampling-based content-aware
BERT model (ColloQL) to achieve robust text-to-SQL modeling over natural
language search (NLS) questions. Due to the lack of evaluation data, we curate
a new dataset of NLS questions and demonstrate the efficacy of our approach.
ColloQL's superior performance extends to well-formed text, achieving 84.9%
(logical) and 90.7% (execution) accuracy on the WikiSQL dataset, making it, to
the best of our knowledge, the highest performing model that does not use
execution guided decoding.

使用数据增强技术和基于采样的内容感知 BERT 模型 (ColloQL) 实现了鲁棒的自然语言搜索 (NLS) 查询的文本到 SQL 建模，评估表明该方法优于现有技术。