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

Online social networks are ubiquitous and user-friendly. Nevertheless, it isvital to detect and moderate offensive content to maintain decency and empathy.However, mining social media texts is a complex task since users don't adhereto any fixed patterns. Comments can be written in any combination of languagesand many of them may be low-resource.  In this paper, we present our system for the LT-EDI shared task on detectinghomophobia and transphobia in social media comments. We experiment with anumber of monolingual and multilingual transformer based models such as mBERTalong with a data augmentation technique for tackling class imbalance. Suchpretrained large models have recently shown tremendous success on a variety ofbenchmark tasks in natural language processing. We observe their performance ona carefully annotated, real life dataset of YouTube comments in English as wellas Tamil.  Our submission achieved ranks 9, 6 and 3 with a macro-averaged F1-score of0.42, 0.64 and 0.58 in the English, Tamil and Tamil-English subtasksrespectively. The code for the system has been open sourced.

bitsa_nlp@LT-EDI-ACL2022: Leveraging Pretrained Language Models for Detecting Homophobia and Transphobia in Social Media Comments