Reproduce “SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising” on OpenResearch
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About this paper
Kuan Xu, Yongbo Wang, Yongliang Wang, Zujie Wen, Yang Dong
SeaD, developed by Ant Group, enhances a Transformer-based Text-to-SQL model through schema-aware denoising and an improved execution-guided decoding strategy. It achieves state-of-the-art performance on the WikiSQL benchmark, reaching 87.1% Logical Form Accuracy and 92.7% Execution Accuracy.
Key insights
- Vanilla sequence-to-sequence models can achieve state-of-the-art Text-to-SQL performance if trained with carefully designed, task-oriented denoising objectives, challenging prior assumptions.
- Self-supervised learning through schema-aware denoising effectively teaches the model robust schema linking and structural understanding without complex architectural modifications.
- Refining execution-guided decoding by considering SQL clause sensitivity improves generated SQL quality without significant human effort or computational overhead.
Summary by alphaXiv.
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