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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The prevalence of toxic content on social media platforms, such as hatespeech, offensive language, and misogyny, presents serious challenges to ourinterconnected society. These challenging issues have attracted widespreadattention in Natural Language Processing (NLP) community. In this paper, wepresent the submitted systems to the first Arabic Misogyny Identificationshared task. We investigate three multi-task learning models as well as theirsingle-task counterparts. In order to encode the input text, our models rely onthe pre-trained MARBERT language model. The overall obtained results show thatall our submitted models have achieved the best performances (top three rankedsubmissions) in both misogyny identification and categorization tasks.

Deep Multi-Task Models for Misogyny Identification and Categorization on Arabic Social Media