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

• URL: http://arxiv.org/abs/2010.09927v1
• Date: Mon, 19 Oct 2020 23:53:17 GMT
• Title: ColloQL: Robust Cross-Domain Text-to-SQL Over Search Queries
• Authors: Karthik Radhakrishnan, Arvind Srikantan, Xi Victoria Lin
• Abstract summary: We introduce data augmentation techniques and a sampling-based content-aware BERT model (ColloQL) ColloQL achieves 84.9% (execution) and 90.7% (execution) accuracy on the Wikilogical dataset.
• Score: 10.273545005890496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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