Jan. 4, 2022, 9:10 p.m. | Sean Benhur, Roshan Nayak, Kanchana Sivanraju, Adeep Hande, Subalalitha Chinnaudayar Navaneethakrishnan, Ruba Priyadharshini, Bharathi Raja Chakravart

cs.CL updates on arXiv.org arxiv.org

Due to the exponentially increasing reach of social media, it is essential to
focus on its negative aspects as it can potentially divide society and incite
people into violence. In this paper, we present our system description of work
on the shared task ComMA@ICON, where we have to classify how aggressive the
sentence is and if the sentence is gender-biased or communal biased. These
three could be the primary reasons to cause significant problems in society. As
team Hypers we …

arxiv bias gender gender bias modelling

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