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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

A new method for Text-to-SQL parsing, Grammar Pre-training (GP), is proposedto decode deep relations between question and database. Firstly, to betterutilize the information of databases, a random value is added behind a questionword which is recognized as a column, and the new sentence serves as the modelinput. Secondly, initialization of vectors for decoder part is optimized, withreference to the former encoding so that question information can be concerned.Finally, a new approach called flooding level is adopted to get the non-zerotraining loss which can generalize better results. By encoding the sentencewith GRAPPA and RAT-SQL model, we achieve better performance on spider, across-DB Text-to-SQL dataset (72.8 dev, 69.8 test). Experiments show that ourmethod is easier to converge during training and has excellent robustness.

GP: Context-free Grammar Pre-training for Text-to-SQL Parsers