@inproceedings{gandhi-etal-2021-beware,
title = "Beware Haters at {C}om{MA}@{ICON}: Sequence and Ensemble Classifiers for Aggression, Gender Bias and Communal Bias Identification in {I}ndian Languages",
author = "Gandhi, Deepakindresh and
Ambalavanan, Aakash and
Rohan, Avireddy and
Selvamani, Radhika",
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.4",
pages = "26--34",
abstract = "Aggressive and hate-filled messages are prevalent on the internet more than ever. These messages are being targeted against a person or an event online and making the internet a more hostile environment. Since this issue is widespread across many users and is not only limited to one language, there is a need for automated models with multilingual capabilities to detect such hostile messages on the online platform. In this paper, the performance of our classifiers is described in the Shared Task on Multilingual Gender Biased and Communal Language Identification at ICON 2021. Our team {``}Beware Haters{''} took part in Hindi, Bengali, Meitei, and Multilingual tasks. Our team used various models like Random Forest, Logistic Regression, Bidirectional Long Short Term Memory, and an ensemble model. Model interpretation tool LIME was used before integrating the models. The instance F1 score of our best performing models for Hindi, Bengali, Meitei, and Multilingual tasks are 0.289, 0.292, 0.322, and 0.294 respectively.",
}
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<abstract>Aggressive and hate-filled messages are prevalent on the internet more than ever. These messages are being targeted against a person or an event online and making the internet a more hostile environment. Since this issue is widespread across many users and is not only limited to one language, there is a need for automated models with multilingual capabilities to detect such hostile messages on the online platform. In this paper, the performance of our classifiers is described in the Shared Task on Multilingual Gender Biased and Communal Language Identification at ICON 2021. Our team “Beware Haters” took part in Hindi, Bengali, Meitei, and Multilingual tasks. Our team used various models like Random Forest, Logistic Regression, Bidirectional Long Short Term Memory, and an ensemble model. Model interpretation tool LIME was used before integrating the models. The instance F1 score of our best performing models for Hindi, Bengali, Meitei, and Multilingual tasks are 0.289, 0.292, 0.322, and 0.294 respectively.</abstract>
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%0 Conference Proceedings
%T Beware Haters at ComMA@ICON: Sequence and Ensemble Classifiers for Aggression, Gender Bias and Communal Bias Identification in Indian Languages
%A Gandhi, Deepakindresh
%A Ambalavanan, Aakash
%A Rohan, Avireddy
%A Selvamani, Radhika
%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 gandhi-etal-2021-beware
%X Aggressive and hate-filled messages are prevalent on the internet more than ever. These messages are being targeted against a person or an event online and making the internet a more hostile environment. Since this issue is widespread across many users and is not only limited to one language, there is a need for automated models with multilingual capabilities to detect such hostile messages on the online platform. In this paper, the performance of our classifiers is described in the Shared Task on Multilingual Gender Biased and Communal Language Identification at ICON 2021. Our team “Beware Haters” took part in Hindi, Bengali, Meitei, and Multilingual tasks. Our team used various models like Random Forest, Logistic Regression, Bidirectional Long Short Term Memory, and an ensemble model. Model interpretation tool LIME was used before integrating the models. The instance F1 score of our best performing models for Hindi, Bengali, Meitei, and Multilingual tasks are 0.289, 0.292, 0.322, and 0.294 respectively.
%U https://aclanthology.org/2021.icon-multigen.4
%P 26-34
Markdown (Informal)
[Beware Haters at ComMA@ICON: Sequence and Ensemble Classifiers for Aggression, Gender Bias and Communal Bias Identification in Indian Languages](https://aclanthology.org/2021.icon-multigen.4) (Gandhi et al., ICON 2021)
ACL