Cn Subalalitha


Hypers at ComMA@ICON: Modelling Aggressive, Gender Bias and Communal Bias Identification
Sean Benhur | Roshan Nayak | Kanchana Sivanraju | Adeep Hande | Cn Subalalitha | Ruba Priyadharshini | Bharathi Raja Chakravarthi
Proceedings of the 18th International Conference on Natural Language Processing: Shared Task on Multilingual Gender Biased and Communal Language Identification

Due to the exponential 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. Our approach utilizes different pretrained models with Attention and mean pooling methods. We were able to get Rank 1 with 0.253 Instance F1 score on Bengali, Rank 2 with 0.323 Instance F1 score on multilingual set, Rank 4 with 0.129 Instance F1 score on meitei and Rank 5 with 0.336 Instance F1 score on Hindi. The source code and the pretrained models of this work can be found here.