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 skeleton (\emph{i.e.}, SQL keywords) and the schema items (\emph{i.e.}, tables and columns). 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 skeleton parsing. The encoder is injected by the most relevant schema items instead of the whole unordered ones to alleviate the schema linking effort during SQL parsing. The decoder first generates the skeleton and then the actual SQL query, which could constrain the SQL parsing. We evaluate the proposed framework on Spider, a well-adopted challenging Text-to-SQL benchmark, and achieve new state-of-the-art (SOTA) results among all the formally published methods.

Decoupling the Skeleton Parsing and Schema Linking for Text-to-SQL

SNLP: Generation / Summarization

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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Haoyang Li

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Decoupling the Skeleton Parsing and Schema Linking for Text-to-SQL

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