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2021

SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising

Xu, Kuan, Wang, Yongbo, Wang, Yongliang et al.

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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.

SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising · Around

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