  Current SQL generators based on pre-trained language models struggle to
answer complex questions requiring domain context or understanding fine-grained
table structure. Humans would deal with these unknowns by reasoning over the
documentation of the tables. Based on this hypothesis, we propose DocuT5, which
uses off-the-shelf language model architecture and injects knowledge from
external `documentation' to improve domain generalization. We perform
experiments on the Spider family of datasets that contain complex questions
that are cross-domain and multi-table. Specifically, we develop a new
text-to-SQL failure taxonomy and find that 19.6% of errors are due to foreign
key mistakes, and 49.2% are due to a lack of domain knowledge. We proposed
DocuT5, a method that captures knowledge from (1) table structure context of
foreign keys and (2) domain knowledge through contextualizing tables and
columns. Both types of knowledge improve over state-of-the-art T5 with
constrained decoding on Spider, and domain knowledge produces state-of-the-art
comparable effectiveness on Spider-DK and Spider-SYN datasets.
