@inproceedings{dowlagar-mamidi-2021-offlangone,
title = "{OFFL}ang{O}ne@{D}ravidian{L}ang{T}ech-{EACL}2021: Transformers with the Class Balanced Loss for Offensive Language Identification in {D}ravidian Code-Mixed text.",
author = "Dowlagar, Suman and
Mamidi, Radhika",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Kumar M, Anand and
Krishnamurthy, Parameswari and
Sherly, Elizabeth",
booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
month = apr,
year = "2021",
address = "Kyiv",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.dravidianlangtech-1.19",
pages = "154--159",
abstract = "The intensity of online abuse has increased in recent years. Automated tools are being developed to prevent the use of hate speech and offensive content. Most of the technologies use natural language and machine learning tools to identify offensive text. In a multilingual society, where code-mixing is a norm, the hate content would be delivered in a code-mixed form in social media, which makes the offensive content identification, further challenging. In this work, we participated in the EACL task to detect offensive content in the code-mixed social media scenario. The methodology uses a transformer model with transliteration and class balancing loss for offensive content identification. In this task, our model has been ranked 2nd in Malayalam-English and 4th in Tamil-English code-mixed languages.",
}
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<abstract>The intensity of online abuse has increased in recent years. Automated tools are being developed to prevent the use of hate speech and offensive content. Most of the technologies use natural language and machine learning tools to identify offensive text. In a multilingual society, where code-mixing is a norm, the hate content would be delivered in a code-mixed form in social media, which makes the offensive content identification, further challenging. In this work, we participated in the EACL task to detect offensive content in the code-mixed social media scenario. The methodology uses a transformer model with transliteration and class balancing loss for offensive content identification. In this task, our model has been ranked 2nd in Malayalam-English and 4th in Tamil-English code-mixed languages.</abstract>
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%0 Conference Proceedings
%T OFFLangOne@DravidianLangTech-EACL2021: Transformers with the Class Balanced Loss for Offensive Language Identification in Dravidian Code-Mixed text.
%A Dowlagar, Suman
%A Mamidi, Radhika
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Kumar M, Anand
%Y Krishnamurthy, Parameswari
%Y Sherly, Elizabeth
%S Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages
%D 2021
%8 April
%I Association for Computational Linguistics
%C Kyiv
%F dowlagar-mamidi-2021-offlangone
%X The intensity of online abuse has increased in recent years. Automated tools are being developed to prevent the use of hate speech and offensive content. Most of the technologies use natural language and machine learning tools to identify offensive text. In a multilingual society, where code-mixing is a norm, the hate content would be delivered in a code-mixed form in social media, which makes the offensive content identification, further challenging. In this work, we participated in the EACL task to detect offensive content in the code-mixed social media scenario. The methodology uses a transformer model with transliteration and class balancing loss for offensive content identification. In this task, our model has been ranked 2nd in Malayalam-English and 4th in Tamil-English code-mixed languages.
%U https://aclanthology.org/2021.dravidianlangtech-1.19
%P 154-159
Markdown (Informal)
[OFFLangOne@DravidianLangTech-EACL2021: Transformers with the Class Balanced Loss for Offensive Language Identification in Dravidian Code-Mixed text.](https://aclanthology.org/2021.dravidianlangtech-1.19) (Dowlagar & Mamidi, DravidianLangTech 2021)
ACL