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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This paper shows that standard assessment methodology for style transfer hasseveral significant problems. First, the standard metrics for style accuracyand semantics preservation vary significantly on different re-runs. Thereforeone has to report error margins for the obtained results. Second, starting withcertain values of bilingual evaluation understudy (BLEU) between input andoutput and accuracy of the sentiment transfer the optimization of these twostandard metrics diverge from the intuitive goal of the style transfer task.Finally, due to the nature of the task itself, there is a specific dependencebetween these two metrics that could be easily manipulated. Under thesecircumstances, we suggest taking BLEU between input and human-writtenreformulations into consideration for benchmarks. We also propose three newarchitectures that outperform state of the art in terms of this metric.

Style Transfer for Texts: Retrain, Report Errors, Compare with Rewrites