  Recently, there has been significant progress in studying neural networks for
translating text descriptions into SQL queries under the zero-shot cross-domain
setting. Despite achieving good performance on some public benchmarks, we
observe that existing text-to-SQL models do not generalize when facing domain
knowledge that does not frequently appear in the training data, which may
render the worse prediction performance for unseen domains. In this work, we
investigate the robustness of text-to-SQL models when the questions require
rarely observed domain knowledge. In particular, we define five types of domain
knowledge and introduce Spider-DK (DK is the abbreviation of domain knowledge),
a human-curated dataset based on the Spider benchmark for text-to-SQL
translation. NL questions in Spider-DK are selected from Spider, and we modify
some samples by adding domain knowledge that reflects real-world question
paraphrases. We demonstrate that the prediction accuracy dramatically drops on
samples that require such domain knowledge, even if the domain knowledge
appears in the training set, and the model provides the correct predictions for
related training samples.
