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

Identifying check-worthy claims is often the first step of automatedfact-checking systems. Tackling this task in a multilingual setting has beenunderstudied. Encoding inputs with multilingual text representations could beone approach to solve the multilingual check-worthiness detection. However,this approach could suffer if cultural bias exists within the communities ondetermining what is check-worthy.In this paper, we propose a languageidentification task as an auxiliary task to mitigate unintended bias.With thispurpose, we experiment joint training by using the datasets from CLEF-2021CheckThat!, that contain tweets in English, Arabic, Bulgarian, Spanish andTurkish. Our results show that joint training of language identification andcheck-worthy claim detection tasks can provide performance gains for some ofthe selected languages.

UPV at CheckThat! 2021: Mitigating Cultural Differences for Identifying Multilingual Check-worthy Claims