@inproceedings{ullah-etal-2024-fida,
title = "Fida @{D}ravidian{L}ang{T}ech 2024: A Novel Approach to Hate Speech Detection Using Distilbert-base-multilingual-cased",
author = "Ullah, Fida and
Zamir, Muhammad and
Arif, Muhammad and
Ahmad, M. and
Felipe-Riveron, E and
Gelbukh, Alexander",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Nadarajan, Rajeswari and
Ravikiran, Manikandan",
booktitle = "Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = mar,
year = "2024",
address = "St. Julian's, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.dravidianlangtech-1.13",
pages = "85--90",
abstract = "In the contemporary digital landscape, social media has emerged as a prominent means of communication and information dissemination, offering a rapid outreach to a broad audience compared to traditional communication methods. Unfortunately, the escalating prevalence of abusive language and hate speech on these platforms has become a pressing issue. Detecting and addressing such content on the Internet has garnered considerable attention due to the significant impact it has on individuals. The advent of deep learning has facilitated the use of pre-trained deep neural network models for text classification tasks. While these models demonstrate high performance, some exhibit a substantial number of parameters. In the DravidianLangTech@EACL 2024 task, we opted for the Distilbert-base-multilingual-cased model, an enhancement of the BERT model that effectively reduces the number of parameters without compromising performance. This model was selected based on its exceptional results in the task. Our system achieved a commendable Macro F1 score of 0.6369{\%}.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ullah-etal-2024-fida">
<titleInfo>
<title>Fida @DravidianLangTech 2024: A Novel Approach to Hate Speech Detection Using Distilbert-base-multilingual-cased</title>
</titleInfo>
<name type="personal">
<namePart type="given">Fida</namePart>
<namePart type="family">Ullah</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Muhammad</namePart>
<namePart type="family">Zamir</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Muhammad</namePart>
<namePart type="family">Arif</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">M</namePart>
<namePart type="family">Ahmad</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">E</namePart>
<namePart type="family">Felipe-Riveron</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alexander</namePart>
<namePart type="family">Gelbukh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-03</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages</title>
</titleInfo>
<name type="personal">
<namePart type="given">Bharathi</namePart>
<namePart type="given">Raja</namePart>
<namePart type="family">Chakravarthi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ruba</namePart>
<namePart type="family">Priyadharshini</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anand</namePart>
<namePart type="given">Kumar</namePart>
<namePart type="family">Madasamy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sajeetha</namePart>
<namePart type="family">Thavareesan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Elizabeth</namePart>
<namePart type="family">Sherly</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rajeswari</namePart>
<namePart type="family">Nadarajan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Manikandan</namePart>
<namePart type="family">Ravikiran</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">St. Julian’s, Malta</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In the contemporary digital landscape, social media has emerged as a prominent means of communication and information dissemination, offering a rapid outreach to a broad audience compared to traditional communication methods. Unfortunately, the escalating prevalence of abusive language and hate speech on these platforms has become a pressing issue. Detecting and addressing such content on the Internet has garnered considerable attention due to the significant impact it has on individuals. The advent of deep learning has facilitated the use of pre-trained deep neural network models for text classification tasks. While these models demonstrate high performance, some exhibit a substantial number of parameters. In the DravidianLangTech@EACL 2024 task, we opted for the Distilbert-base-multilingual-cased model, an enhancement of the BERT model that effectively reduces the number of parameters without compromising performance. This model was selected based on its exceptional results in the task. Our system achieved a commendable Macro F1 score of 0.6369%.</abstract>
<identifier type="citekey">ullah-etal-2024-fida</identifier>
<location>
<url>https://aclanthology.org/2024.dravidianlangtech-1.13</url>
</location>
<part>
<date>2024-03</date>
<extent unit="page">
<start>85</start>
<end>90</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Fida @DravidianLangTech 2024: A Novel Approach to Hate Speech Detection Using Distilbert-base-multilingual-cased
%A Ullah, Fida
%A Zamir, Muhammad
%A Arif, Muhammad
%A Ahmad, M.
%A Felipe-Riveron, E.
%A Gelbukh, Alexander
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Nadarajan, Rajeswari
%Y Ravikiran, Manikandan
%S Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F ullah-etal-2024-fida
%X In the contemporary digital landscape, social media has emerged as a prominent means of communication and information dissemination, offering a rapid outreach to a broad audience compared to traditional communication methods. Unfortunately, the escalating prevalence of abusive language and hate speech on these platforms has become a pressing issue. Detecting and addressing such content on the Internet has garnered considerable attention due to the significant impact it has on individuals. The advent of deep learning has facilitated the use of pre-trained deep neural network models for text classification tasks. While these models demonstrate high performance, some exhibit a substantial number of parameters. In the DravidianLangTech@EACL 2024 task, we opted for the Distilbert-base-multilingual-cased model, an enhancement of the BERT model that effectively reduces the number of parameters without compromising performance. This model was selected based on its exceptional results in the task. Our system achieved a commendable Macro F1 score of 0.6369%.
%U https://aclanthology.org/2024.dravidianlangtech-1.13
%P 85-90
Markdown (Informal)
[Fida @DravidianLangTech 2024: A Novel Approach to Hate Speech Detection Using Distilbert-base-multilingual-cased](https://aclanthology.org/2024.dravidianlangtech-1.13) (Ullah et al., DravidianLangTech-WS 2024)
ACL
- Fida Ullah, Muhammad Zamir, Muhammad Arif, M. Ahmad, E Felipe-Riveron, and Alexander Gelbukh. 2024. Fida @DravidianLangTech 2024: A Novel Approach to Hate Speech Detection Using Distilbert-base-multilingual-cased. In Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 85–90, St. Julian's, Malta. Association for Computational Linguistics.