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.

翻译：在文本到SQL任务中，序列到序列（seq-to-seq）模型因其架构限制常导致次优性能。本文提出一种简单且有效的方法，将基于Transformer的seq-to-seq模型适配为鲁棒的文本到SQL生成系统。我们不采用约束解码器或将其重构为槽填充任务的方式，而是提出带有模式感知去噪（Schema-aware Denoising, SeaD）的训练方法，包含两种去噪目标：通过两种新颖的腐蚀噪声和洗牌噪声训练模型，使其能够恢复输入或预测输出。这些去噪目标作为辅助任务，旨在更有效地建模seq-to-seq生成中的结构化数据。此外，我们改进并提出一种子句敏感的执行为导向（Execution Guided, EG）解码策略，以克服EG解码在生成式模型中的局限性。实验表明，所提方法在模式链接和语法正确性两方面均提升了seq-to-seq模型的性能，并在WikiSQL基准测试上达到最新最优水平。结果表明，朴素seq-to-seq架构在文本到SQL任务中的潜力可能被低估了。