English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

One of the recent best attempts at Text-to-SQL is the pre-trained languagemodel. Due to the structural property of the SQL queries, the seq2seq modeltakes the responsibility of parsing both the schema items (i.e., tables andcolumns) and the skeleton (i.e., SQL keywords). Such coupled targets increasethe difficulty of parsing the correct SQL queries especially when they involvemany schema items and logic operators. This paper proposes a ranking-enhancedencoding and skeleton-aware decoding framework to decouple the schema linkingand the skeleton parsing. Specifically, for a seq2seq encoder-decode model, itsencoder is injected by the most relevant schema items instead of the wholeunordered ones, which could alleviate the schema linking effort during SQLparsing, and its decoder first generates the skeleton and then the actual SQLquery, which could implicitly constrain the SQL parsing. We evaluate ourproposed framework on Spider and its three robustness variants: Spider-DK,Spider-Syn, and Spider-Realistic. The experimental results show that ourframework delivers promising performance and robustness. Our code is availableat https://github.com/RUCKBReasoning/RESDSQL.

RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL