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KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers
[article]
2021
arXiv
pre-print
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
arXiv:2106.11455v1
fatcat:qx6777rqsbckzocoxna474u7nm