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

Synthesizing SQL queries from natural language is a long-standing openproblem and has been attracting considerable interest recently. Toward solvingthe problem, the de facto approach is to employ a sequence-to-sequence-stylemodel. Such an approach will necessarily require the SQL queries to beserialized. Since the same SQL query may have multiple equivalentserializations, training a sequence-to-sequence-style model is sensitive to thechoice from one of them. This phenomenon is documented as the "order-matters"problem. Existing state-of-the-art approaches rely on reinforcement learning toreward the decoder when it generates any of the equivalent serializations.However, we observe that the improvement from reinforcement learning islimited.  In this paper, we propose a novel approach, i.e., SQLNet, to fundamentallysolve this problem by avoiding the sequence-to-sequence structure when theorder does not matter. In particular, we employ a sketch-based approach wherethe sketch contains a dependency graph so that one prediction can be done bytaking into consideration only the previous predictions that it depends on. Inaddition, we propose a sequence-to-set model as well as the column attentionmechanism to synthesize the query based on the sketch. By combining all thesenovel techniques, we show that SQLNet can outperform the prior art by 9% to 13%on the WikiSQL task.

SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning