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

Anonymous

Paper Link: https://openreview.net/forum?id=Dgx157I8729

Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)

Abstract: On the WikiSQL benchmark, most methods tackle the challenge of text-to-SQL with predefined sketch slots and build sophisticated sub-tasks to fill these slots. Though achieving promising results, these methods suffer from over-complex model structure. In this paper, we present a simple yet effective approach that enables auto-regressive sequence-to-sequence model to robust text-to-SQL generation. Instead of formulating the task of text-to-SQL as slot-filling, we propose to train sequence-to-sequence 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 model-agnostic denoising objectives act as the auxiliary tasks for structural data modeling during sequence-to-sequence generation. In addition, we 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 sequence-to-sequence model in both schema linking and grammar correctness and establishes new state-of-the-art on WikiSQL benchmark. Our work indicates that the capacity of sequence-to-sequence model for text-to-SQL may have been under-estimated and could be enhanced by specialized denoising task.

Presentation Mode: This paper will be presented virtually

Virtual Presentation Timezone: UTC+8

Copyright Consent Signature (type Name Or NA If Not Transferrable): Kuan Xu

Copyright Consent Name And Address: Ant Group, A Space, No. 569 Xixi Road, Hangzhou, China