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Due to the exponentially increasing reach of social media, it is essential tofocus on its negative aspects as it can potentially divide society and incitepeople into violence. In this paper, we present our system description of workon the shared task ComMA@ICON, where we have to classify how aggressive thesentence is and if the sentence is gender-biased or communal biased. Thesethree could be the primary reasons to cause significant problems in society. Asteam Hypers we have proposed an approach that utilizes different pretrainedmodels with Attention and mean pooling methods. We were able to get Rank 3 with0.223 Instance F1 score on Bengali, Rank 2 with 0.322 Instance F1 score onMulti-lingual set, Rank 4 with 0.129 Instance F1 score on Meitei and Rank 5with 0.336 Instance F1 score on Hindi. The source code and the pretrainedmodels of this work can be found here.

Hypers at ComMA@ICON: Modelling Aggressiveness, Gender Bias and Communal Bias Identification