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

Machine translation (MT) has benefited from using synthetic training dataoriginating from translating monolingual corpora, a technique known asbacktranslation. Combining backtranslated data from different sources has ledto better results than when using such data in isolation. In this work weanalyse the impact that data translated with rule-based, phrase-basedstatistical and neural MT systems has on new MT systems. We use a real-worldlow-resource use-case (Basque-to-Spanish in the clinical domain) as well as ahigh-resource language pair (German-to-English) to test different scenarioswith backtranslation and employ data selection to optimise the syntheticcorpora. We exploit different data selection strategies in order to reduce theamount of data used, while at the same time maintaining high-quality MTsystems. We further tune the data selection method by taking into account thequality of the MT systems used for backtranslation and lexical diversity of theresulting corpora. Our experiments show that incorporating backtranslated datafrom different sources can be beneficial, and that availing of data selectioncan yield improved performance.

Selecting Backtranslated Data from Multiple Sources for Improved Neural Machine Translation