Sean Benhur (2021) Pretrained Transformers for Offensive Language Identification in Tanglish. Hypers at ComMA@ICON: Modelling Aggressiveness, Gender Bias and Communal Bias Identification.

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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
Uncontrolled Keywords: Hate Speech, Offensive Content, BERT, Transformer
Depositing User: Mr Team Mosys
Date Deposited: 12 Sep 2024 04:23
Last Modified: 12 Sep 2024 04:23
URI: https://ir.psgcas.ac.in/id/eprint/2258

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