Optimizing Deeper Transformers on Small Datasets: An Application on Text-to-SQL Semantic Parsing
Peng Xu, Wei Yang, Wenjie Zi, Keyi Tang, Chengyang Huang, J. Cheung, Yanshuai Cao
arXiv
Abstract
Due to the common belief that training deep transformers from scratch requires large datasets, people usually only use shallow and simple additional layers on top of pre-trained models during fine-tuning on small datasets. We provide evidence that this does not al-ways need to be the case: with proper initialization and training techniques, the bene-fits of very deep transformers are shown to carry over to hard structural prediction tasks, even using small datasets. In particular, we successfully train 48 layers of transformers for a semantic parsing task. These com-prise 24 fine-tuned transformer layers from pre-trained RoBERTa and 24 relation-aware transformer layers trained from scratch. With fewer training steps and no task-specific pretraining, we obtain the state of the art performance on the challenging cross-domain Text-to-SQL semantic parsing benchmark Spider. We achieve this by deriving a novel D ata dependent T ransformer Fix ed- up date initialization scheme (DT-Fixup), inspired by the prior T-Fixup work (Huang et al., 2020). Further error analysis demonstrates that increasing the depth of the transformer model can help improve generalization on the cases requiring reasoning and structural understanding 1 .