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

Social media platforms often act as breeding grounds for various forms oftrolling or malicious content targeting users or communities. One way oftrolling users is by creating memes, which in most cases unites an image with ashort piece of text embedded on top of it. The situation is more complex formultilingual(e.g., Tamil) memes due to the lack of benchmark datasets andmodels. We explore several models to detect Troll memes in Tamil based on theshared task, "Troll Meme Classification in DravidianLangTech2022" at ACL-2022.We observe while the text-based model MURIL performs better for Non-troll memeclassification, the image-based model VGG16 performs better for Troll-memeclassification. Further fusing these two modalities help us achieve stableoutcomes in both classes. Our fusion model achieved a 0.561 weighted average F1score and ranked second in this task.

hate-alert@DravidianLangTech-ACL2022: Ensembling Multi-Modalities for Tamil TrollMeme Classification