@inproceedings{benhur-etal-2021-hypers,
title = "Hypers at {C}om{MA}@{ICON}: Modelling Aggressive, Gender Bias and Communal Bias Identification",
author = "Benhur, Sean and
Nayak, Roshan and
Sivanraju, Kanchana and
Hande, Adeep and
Subalalitha, Cn and
Priyadharshini, Ruba and
Chakravarthi, Bharathi Raja",
editor = "Kumar, Ritesh and
Singh, Siddharth and
Nandi, Enakshi and
Ratan, Shyam and
Devi, Laishram Niranjana and
Lahiri, Bornini and
Bansal, Akanksha and
Bhagat, Akash and
Dawer, Yogesh",
booktitle = "Proceedings of the 18th International Conference on Natural Language Processing: Shared Task on Multilingual Gender Biased and Communal Language Identification",
month = dec,
year = "2021",
address = "NIT Silchar",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2021.icon-multigen.3",
pages = "21--25",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Hypers at ComMA@ICON: Modelling Aggressive, Gender Bias and Communal Bias Identification
%A Benhur, Sean
%A Nayak, Roshan
%A Sivanraju, Kanchana
%A Hande, Adeep
%A Subalalitha, Cn
%A Priyadharshini, Ruba
%A Chakravarthi, Bharathi Raja
%Y Kumar, Ritesh
%Y Singh, Siddharth
%Y Nandi, Enakshi
%Y Ratan, Shyam
%Y Devi, Laishram Niranjana
%Y Lahiri, Bornini
%Y Bansal, Akanksha
%Y Bhagat, Akash
%Y Dawer, Yogesh
%S Proceedings of the 18th International Conference on Natural Language Processing: Shared Task on Multilingual Gender Biased and Communal Language Identification
%D 2021
%8 December
%I NLP Association of India (NLPAI)
%C NIT Silchar
%F benhur-etal-2021-hypers
%X 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.
%U https://aclanthology.org/2021.icon-multigen.3
%P 21-25
Markdown (Informal)
[Hypers at ComMA@ICON: Modelling Aggressive, Gender Bias and Communal Bias Identification](https://aclanthology.org/2021.icon-multigen.3) (Benhur et al., ICON 2021)
ACL
- Sean Benhur, Roshan Nayak, Kanchana Sivanraju, Adeep Hande, Cn Subalalitha, Ruba Priyadharshini, and Bharathi Raja Chakravarthi. 2021. Hypers at ComMA@ICON: Modelling Aggressive, Gender Bias and Communal Bias Identification. In Proceedings of the 18th International Conference on Natural Language Processing: Shared Task on Multilingual Gender Biased and Communal Language Identification, pages 21–25, NIT Silchar. NLP Association of India (NLPAI).