  Text-to-SQL is a crucial task toward developing methods for understanding
natural language by computers. Recent neural approaches deliver excellent
performance; however, models that are difficult to interpret inhibit future
developments. Hence, this study aims to provide a better approach toward the
interpretation of neural models. We hypothesize that the internal behavior of
models at hand becomes much easier to analyze if we identify the detailed
performance of schema linking simultaneously as the additional information of
the text-to-SQL performance. We provide the ground-truth annotation of schema
linking information onto the Spider dataset. We demonstrate the usefulness of
the annotated data and how to analyze the current state-of-the-art neural
models.
