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

Disentangling the content and style in the latent space is prevalent inunpaired text style transfer. However, two major issues exist in most of thecurrent neural models. 1) It is difficult to completely strip the styleinformation from the semantics for a sentence. 2) The recurrent neural network(RNN) based encoder and decoder, mediated by the latent representation, cannotwell deal with the issue of the long-term dependency, resulting in poorpreservation of non-stylistic semantic content. In this paper, we propose theStyle Transformer, which makes no assumption about the latent representation ofsource sentence and equips the power of attention mechanism in Transformer toachieve better style transfer and better content preservation.

Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation