KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers

• URL: http://arxiv.org/abs/2106.11455v1
• Date: Tue, 22 Jun 2021 00:08:03 GMT
• Title: KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers
• Authors: Chia-Hsuan Lee, Oleksandr Polozov, Matthew Richardson
• Abstract summary: We present KDBaggleQA, a new cross-domain evaluation dataset of real Web databases. We show that KDBaggleQA presents a challenge to state-of-the-art zero-shots but that a more realistic evaluation setting and creative use of associated database documentation boosts their accuracy by over 13.2%.
• Score: 26.15889661083109
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The goal of database question answering is to enable natural language querying of real-life relational databases in diverse application domains. Recently, large-scale datasets such as Spider and WikiSQL facilitated novel modeling techniques for text-to-SQL parsing, improving zero-shot generalization to unseen databases. In this work, we examine the challenges that still prevent these techniques from practical deployment. First, we present KaggleDBQA, a new cross-domain evaluation dataset of real Web databases, with domain-specific data types, original formatting, and unrestricted questions. Second, we re-examine the choice of evaluation tasks for text-to-SQL parsers as applied in real-life settings. Finally, we augment our in-domain evaluation task with database documentation, a naturally occurring source of implicit domain knowledge. We show that KaggleDBQA presents a challenge to state-of-the-art zero-shot parsers but a more realistic evaluation setting and creative use of associated database documentation boosts their accuracy by over 13.2%, doubling their performance.

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