ColloQL: Robust Cross-Domain Text-to-SQL Over Search Queries

Understand

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.