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

Deep neural language models such as BERT have enabled substantial recentadvances in many natural language processing tasks. Due to the effort andcomputational cost involved in their pre-training, language-specific models aretypically introduced only for a small number of high-resource languages such asEnglish. While multilingual models covering large numbers of languages areavailable, recent work suggests monolingual training can produce better models,and our understanding of the tradeoffs between mono- and multilingual trainingis incomplete. In this paper, we introduce a simple, fully automated pipelinefor creating language-specific BERT models from Wikipedia data and introduce 42new such models, most for languages up to now lacking dedicated deep neurallanguage models. We assess the merits of these models using thestate-of-the-art UDify parser on Universal Dependencies data, contrastingperformance with results using the multilingual BERT model. We find that UDifyusing WikiBERT models outperforms the parser using mBERT on average, with thelanguage-specific models showing substantially improved performance for somelanguages, yet limited improvement or a decrease in performance for others. Wealso present preliminary results as first steps toward an understanding of theconditions under which language-specific models are most beneficial. All of themethods and models introduced in this work are available under open licensesfrom https://github.com/turkunlp/wikibert.

WikiBERT models: deep transfer learning for many languages