Sean Benhur (2022) Hypers at ComMA@ICON: Modelling Aggressiveness, Gender Bias and Communal Bias Identification. Hypers at ComMA@ICON: Modelling Aggressiveness, Gender Bias and Communal Bias Identification. pp. 21-25.

[thumbnail of Hypers at ComMA@ICON Modelling AggressivenessGender Bias.pdf] Text
Hypers at ComMA@ICON Modelling AggressivenessGender Bias.pdf - Published Version
Download (101kB)

Abstract

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 have proposed
an approach which utilizes different pretrained
models with Attention and mean poolingmethods.
We were able to get Rank 3 with 0.223 Instance
F1 score on Bengali, Rank 2 with 0.322
Instance F1 score on Multi-lingual 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 here1

Item Type: Article
Additional Information: format from: http://arxiv.org/ps/2112.15417v2
Depositing User: Mr Team Mosys
Date Deposited: 07 May 2025 06:21
Last Modified: 07 May 2025 06:21
URI: https://ir.psgcas.ac.in/id/eprint/2414

Actions (login required)

View Item
View Item