  It is a common belief that training deep transformers from scratch requires
large datasets. Consequently, for small datasets, people usually use shallow
and simple additional layers on top of pre-trained models during fine-tuning.
This work shows that this does not always need to be the case: with proper
initialization and optimization, the benefits of very deep transformers can
carry over to challenging tasks with small datasets, including Text-to-SQL
semantic parsing and logical reading comprehension. In particular, we
successfully train $48$ layers of transformers, comprising $24$ fine-tuned
layers from pre-trained RoBERTa and $24$ relation-aware layers trained from
scratch. With fewer training steps and no task-specific pre-training, we obtain
the state-of-the-art performance on the challenging cross-domain Text-to-SQL
parsing benchmark Spider. We achieve this by deriving a novel Data-dependent
Transformer Fixed-update initialization scheme (DT-Fixup), inspired by the
prior T-Fixup work. Further error analysis shows that increasing depth can help
improve generalization on small datasets for hard cases that require reasoning
and structural understanding.
