SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising
Kuan Xu, Yongbo Wang, Yongliang Wang, Zihao Wang, Zujie Wen, Yang Dong
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.- Anthology ID:
- 2022.findings-naacl.141
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2022
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1845–1853
- Language:
- URL:
- https://aclanthology.org/2022.findings-naacl.141
- DOI:
- 10.18653/v1/2022.findings-naacl.141
- Cite (ACL):
- Kuan Xu, Yongbo Wang, Yongliang Wang, Zihao Wang, Zujie Wen, and Yang Dong. 2022. SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1845–1853, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising (Xu et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.findings-naacl.141.pdf
- Code
- salesforce/WikiSQL
- Data
- WikiSQL