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

In many neural models, new features as polynomial functions of existing onesare used to augment representations. Using the natural language inference taskas an example, we investigate the use of scaled polynomials of degree 2 andabove as matching features. We find that scaling degree 2 features has thehighest impact on performance, reducing classification error by 5% in the bestmodels.

On the scaling of polynomial features for representation matching