One of the recent best attempts at Text-to-SQL is the pre-trained language
model. Due to the structural property of the SQL queries, the seq2seq model
takes the responsibility of parsing both the schema items (i.e., tables and
columns) and the skeleton (i.e., SQL keywords). Such coupled targets increase
the difficulty of parsing the correct SQL queries especially when they involve
many schema items and logic operators. This paper proposes a ranking-enhanced
encoding and skeleton-aware decoding framework to decouple the schema linking
and the skeleton parsing. Specifically, for a seq2seq encoder-decode model, its
encoder is injected by the most relevant schema items instead of the whole
unordered ones, which could alleviate the schema linking effort during SQL
parsing, and its decoder first generates the skeleton and then the actual SQL
query, which could implicitly constrain the SQL parsing. We evaluate our
proposed framework on Spider and its three robustness variants: Spider-DK,
Spider-Syn, and Spider-Realistic. The experimental results show that our
framework delivers promising performance and robustness. Our code is available
at this https URL.

本研究提出一种基于预训练语言模型的 Text-to-SQL 模型，结合排序增强编码和骨架感知解码框架的方法，以解耦数据库模式连接和 SQL 关键字解析，实验表明该模型在 Spider 数据集上表现良好。