@inproceedings{rahman-etal-2024-binary,
title = "{B}inary{\_}{B}easts@{D}ravidian{L}ang{T}ech-{EACL} 2024: Multimodal Abusive Language Detection in {T}amil based on Integrated Approach of Machine Learning and Deep Learning Techniques",
author = "Rahman, Md. and
Raihan, Abu and
Rahman, Tanzim and
Ahsan, Shawly and
Hossain, Jawad and
Das, Avishek and
Hoque, Mohammed Moshiul",
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-1.35",
pages = "212--217",
abstract = "Detecting abusive language on social media is a challenging task that needs to be solved effectively. This research addresses the formidable challenge of detecting abusive language in Tamil through a comprehensive multimodal approach, incorporating textual, acoustic, and visual inputs. This study utilized ConvLSTM, 3D-CNN, and a hybrid 3D-CNN with BiLSTM to extract video features. Several models, such as BiLSTM, LR, and CNN, are explored for processing audio data, whereas for textual content, MNB, LR, and LSTM methods are explored. To further enhance overall performance, this work introduced a weighted late fusion model amalgamating predictions from all modalities. The fusion model was then applied to make predictions on the test dataset. The ConvLSTM+BiLSTM+MNB model yielded the highest macro F1 score of 71.43{\%}. Our methodology allowed us to achieve 1 st rank for multimodal abusive language detection in the shared task",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="rahman-etal-2024-binary">
<titleInfo>
<title>Binary_Beasts@DravidianLangTech-EACL 2024: Multimodal Abusive Language Detection in Tamil based on Integrated Approach of Machine Learning and Deep Learning Techniques</title>
</titleInfo>
<name type="personal">
<namePart type="given">Md.</namePart>
<namePart type="family">Rahman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Abu</namePart>
<namePart type="family">Raihan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tanzim</namePart>
<namePart type="family">Rahman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shawly</namePart>
<namePart type="family">Ahsan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jawad</namePart>
<namePart type="family">Hossain</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Avishek</namePart>
<namePart type="family">Das</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mohammed</namePart>
<namePart type="given">Moshiul</namePart>
<namePart type="family">Hoque</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>Detecting abusive language on social media is a challenging task that needs to be solved effectively. This research addresses the formidable challenge of detecting abusive language in Tamil through a comprehensive multimodal approach, incorporating textual, acoustic, and visual inputs. This study utilized ConvLSTM, 3D-CNN, and a hybrid 3D-CNN with BiLSTM to extract video features. Several models, such as BiLSTM, LR, and CNN, are explored for processing audio data, whereas for textual content, MNB, LR, and LSTM methods are explored. To further enhance overall performance, this work introduced a weighted late fusion model amalgamating predictions from all modalities. The fusion model was then applied to make predictions on the test dataset. The ConvLSTM+BiLSTM+MNB model yielded the highest macro F1 score of 71.43%. Our methodology allowed us to achieve 1 st rank for multimodal abusive language detection in the shared task</abstract>
<identifier type="citekey">rahman-etal-2024-binary</identifier>
<location>
<url>https://aclanthology.org/2024.dravidianlangtech-1.35</url>
</location>
<part>
<date>2024-03</date>
<extent unit="page">
<start>212</start>
<end>217</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Binary_Beasts@DravidianLangTech-EACL 2024: Multimodal Abusive Language Detection in Tamil based on Integrated Approach of Machine Learning and Deep Learning Techniques
%A Rahman, Md.
%A Raihan, Abu
%A Rahman, Tanzim
%A Ahsan, Shawly
%A Hossain, Jawad
%A Das, Avishek
%A Hoque, Mohammed Moshiul
%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 rahman-etal-2024-binary
%X Detecting abusive language on social media is a challenging task that needs to be solved effectively. This research addresses the formidable challenge of detecting abusive language in Tamil through a comprehensive multimodal approach, incorporating textual, acoustic, and visual inputs. This study utilized ConvLSTM, 3D-CNN, and a hybrid 3D-CNN with BiLSTM to extract video features. Several models, such as BiLSTM, LR, and CNN, are explored for processing audio data, whereas for textual content, MNB, LR, and LSTM methods are explored. To further enhance overall performance, this work introduced a weighted late fusion model amalgamating predictions from all modalities. The fusion model was then applied to make predictions on the test dataset. The ConvLSTM+BiLSTM+MNB model yielded the highest macro F1 score of 71.43%. Our methodology allowed us to achieve 1 st rank for multimodal abusive language detection in the shared task
%U https://aclanthology.org/2024.dravidianlangtech-1.35
%P 212-217
Markdown (Informal)
[Binary_Beasts@DravidianLangTech-EACL 2024: Multimodal Abusive Language Detection in Tamil based on Integrated Approach of Machine Learning and Deep Learning Techniques](https://aclanthology.org/2024.dravidianlangtech-1.35) (Rahman et al., DravidianLangTech-WS 2024)
ACL
- Md. Rahman, Abu Raihan, Tanzim Rahman, Shawly Ahsan, Jawad Hossain, Avishek Das, and Mohammed Moshiul Hoque. 2024. Binary_Beasts@DravidianLangTech-EACL 2024: Multimodal Abusive Language Detection in Tamil based on Integrated Approach of Machine Learning and Deep Learning Techniques. In Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 212–217, St. Julian's, Malta. Association for Computational Linguistics.