  The importance of building text-to-SQL parsers which can be applied to new
databases has long been acknowledged, and a critical step to achieve this goal
is schema linking, i.e., properly recognizing mentions of unseen columns or
tables when generating SQLs. In this work, we propose a novel framework to
elicit relational structures from large-scale pre-trained language models
(PLMs) via a probing procedure based on Poincar\'e distance metric, and use the
induced relations to augment current graph-based parsers for better schema
linking. Compared with commonly-used rule-based methods for schema linking, we
found that probing relations can robustly capture semantic correspondences,
even when surface forms of mentions and entities differ. Moreover, our probing
procedure is entirely unsupervised and requires no additional parameters.
Extensive experiments show that our framework sets new state-of-the-art
performance on three benchmarks. We empirically verify that our probing
procedure can indeed find desired relational structures through qualitative
analysis. Our code can be found at
https://github.com/AlibabaResearch/DAMO-ConvAI.
