  In text-to-SQL task, seq-to-seq models often lead to sub-optimal performance
due to limitations in their architecture. In this paper, we present a simple
yet effective approach that adapts transformer-based seq-to-seq model to robust
text-to-SQL generation. Instead of inducing constraint to decoder or reformat
the task as slot-filling, we propose to train seq-to-seq model with Schema
aware Denoising (SeaD), which consists of two denoising objectives that train
model to either recover input or predict output from two novel erosion and
shuffle noises. These denoising objectives acts as the auxiliary tasks for
better modeling the structural data in S2S generation. In addition, we improve
and propose a clause-sensitive execution guided (EG) decoding strategy to
overcome the limitation of EG decoding for generative model. The experiments
show that the proposed method improves the performance of seq-to-seq model in
both schema linking and grammar correctness and establishes new
state-of-the-art on WikiSQL benchmark. The results indicate that the capacity
of vanilla seq-to-seq architecture for text-to-SQL may have been
under-estimated.
