Domain Adaptation of a State of the Art Text-to-SQL Model: Lessons
  Learned and Challenges Found
Irene Manotas Octavian Popescu Ngoc Phuoc An Vo Vadim Sheinin
There are many recent advanced developments for the Text-to-SQL task, where
the Picard model is one of the the top performing models as measured by the
Spider dataset competition. However, bringing Text-to-SQL systems to realistic
use-cases through domain adaptation remains a tough challenge. We analyze how
well the base T5 Language Model and Picard perform on query structures
different from the Spider dataset, we fine-tuned the base model on the Spider
data and on independent databases (DB). To avoid accessing the DB content
online during inference, we also present an alternative way to disambiguate the
values in an input question using a rule-based approach that relies on an
intermediate representation of the semantic concepts of an input question. In
our results we show in what cases T5 and Picard can deliver good performance,
we share the lessons learned, and discuss current domain adaptation challenges.