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

Knowledge graphs (KGs) can vary greatly from one domain to another. Thereforesupervised approaches to both graph-to-text generation and text-to-graphknowledge extraction (semantic parsing) will always suffer from a shortage ofdomain-specific parallel graph-text data; at the same time, adapting a modeltrained on a different domain is often impossible due to little or no overlapin entities and relations. This situation calls for an approach that (1) doesnot need large amounts of annotated data and thus (2) does not need to rely ondomain adaptation techniques to work well in different domains. To this end, wepresent the first approach to unsupervised text generation from KGs and showsimultaneously how it can be used for unsupervised semantic parsing. Weevaluate our approach on WebNLG v2.1 and a new benchmark leveraging scenegraphs from Visual Genome. Our system outperforms strong baselines for bothtext$\leftrightarrow$graph conversion tasks without any manual adaptation fromone dataset to the other. In additional experiments, we investigate the impactof using different unsupervised objectives.

An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing