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

Learned metrics such as BLEURT have in recent years become widely employed toevaluate the quality of machine translation systems. Training such metricsrequires data which can be expensive and difficult to acquire, particularly forlower-resource languages. We show how knowledge can be distilled from LargeLanguage Models (LLMs) to improve upon such learned metrics without requiringhuman annotators, by creating synthetic datasets which can be mixed intoexisting datasets, requiring only a corpus of text in the target language. Weshow that the performance of a BLEURT-like model on lower resource languagescan be improved in this way.

Learning Translation Quality Evaluation on Low Resource Languages from Large Language Models