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

We describe the JD Explore Academy's submission of the WMT 2022 sharedgeneral translation task. We participated in all high-resource tracks and onemedium-resource track, including Chinese-English, German-English,Czech-English, Russian-English, and Japanese-English. We push the limit of ourprevious work -- bidirectional training for translation by scaling up two mainfactors, i.e. language pairs and model sizes, namely the \textbf{Vega-MT}system. As for language pairs, we scale the "bidirectional" up to the"multidirectional" settings, covering all participating languages, to exploitthe common knowledge across languages, and transfer them to the downstreambilingual tasks. As for model sizes, we scale the Transformer-Big up to theextremely large model that owns nearly 4.7 Billion parameters, to fully enhancethe model capacity for our Vega-MT. Also, we adopt the data augmentationstrategies, e.g. cycle translation for monolingual data, and bidirectionalself-training for bilingual and monolingual data, to comprehensively exploitthe bilingual and monolingual data. To adapt our Vega-MT to the general domaintest set, generalization tuning is designed. Based on the official automaticscores of constrained systems, in terms of the sacreBLEU shown in Figure-1, wegot the 1st place on {Zh-En (33.5), En-Zh (49.7), De-En (33.7), En-De (37.8),Cs-En (54.9), En-Cs (41.4) and En-Ru (32.7)}, 2nd place on {Ru-En (45.1) andJa-En (25.6)}, and 3rd place on {En-Ja(41.5)}, respectively; W.R.T the COMET,we got the 1st place on {Zh-En (45.1), En-Zh (61.7), De-En (58.0), En-De(63.2), Cs-En (74.7), Ru-En (64.9), En-Ru (69.6) and En-Ja (65.1)}, 2nd placeon {En-Cs (95.3) and Ja-En (40.6)}, respectively.

Vega-MT: The JD Explore Academy Translation System for WMT22