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

We present BRIDGE, a powerful sequential architecture for modelingdependencies between natural language questions and relational databases incross-DB semantic parsing. BRIDGE represents the question and DB schema in atagged sequence where a subset of the fields are augmented with cell valuesmentioned in the question. The hybrid sequence is encoded by BERT with minimalsubsequent layers and the text-DB contextualization is realized via thefine-tuned deep attention in BERT. Combined with a pointer-generator decoderwith schema-consistency driven search space pruning, BRIDGE attainedstate-of-the-art performance on popular cross-DB text-to-SQL benchmarks, Spider(71.1\% dev, 67.5\% test with ensemble model) and WikiSQL (92.6\% dev, 91.9\%test). Our analysis shows that BRIDGE effectively captures the desiredcross-modal dependencies and has the potential to generalize to more text-DBrelated tasks. Our implementation is available at\url{https://github.com/salesforce/TabularSemanticParsing}.

Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing