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

Grammar error correction (GEC) systems have become ubiquitous in a variety ofsoftware applications, and have started to approach human-level performance forsome datasets. However, very little is known about how to efficientlypersonalize these systems to the user's characteristics, such as theirproficiency level and first language, or to emerging domains of text. Wepresent the first results on adapting a general-purpose neural GEC system toboth the proficiency level and the first language of a writer, using only a fewthousand annotated sentences. Our study is the broadest of its kind, coveringfive proficiency levels and twelve different languages, and comparing threedifferent adaptation scenarios: adapting to the proficiency level only, to thefirst language only, or to both aspects simultaneously. We show that tailoringto both scenarios achieves the largest performance improvement (3.6 F0.5)relative to a strong baseline.

Personalizing Grammatical Error Correction: Adaptation to Proficiency Level and L1