@inproceedings{farsi-etal-2024-cuet-binary-hackers,
title = "{CUET}{\_}{B}inary{\_}{H}ackers@{D}ravidian{L}ang{T}ech {EACL}2024: Hate and Offensive Language Detection in {T}elugu Code-Mixed Text Using Sentence Similarity {BERT}",
author = "Farsi, Salman and
Eusha, Asrarul and
Hossain, Jawad and
Ahsan, Shawly 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-1.32",
pages = "193--199",
abstract = "With the continuous evolution of technology and widespread internet access, various social media platforms have gained immense popularity, attracting a vast number of active users globally. However, this surge in online activity has also led to a concerning trend by driving many individuals to resort to posting hateful and offensive comments or posts, publicly targeting groups or individuals. In response to these challenges, we participated in this shared task. Our approach involved proposing a fine-tuning-based pre-trained transformer model to effectively discern whether a given text contains offensive content that propagates hatred. We conducted comprehensive experiments, exploring various machine learning (LR, SVM, and Ensemble), deep learning (CNN, BiLSTM, CNN+BiLSTM), and transformer-based models (Indic-SBERT, m- BERT, MuRIL, Distil-BERT, XLM-R), adhering to a meticulous fine-tuning methodology. Among the models evaluated, our fine-tuned L3Cube-Indic-Sentence-Similarity- BERT or Indic-SBERT model demonstrated superior performance, achieving a macro-average F1-score of 0.7013. This notable result positioned us at the 6th place in the task. The implementation details of the task will be found in the GitHub repository.",
}
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<namePart type="given">Elizabeth</namePart>
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<abstract>With the continuous evolution of technology and widespread internet access, various social media platforms have gained immense popularity, attracting a vast number of active users globally. However, this surge in online activity has also led to a concerning trend by driving many individuals to resort to posting hateful and offensive comments or posts, publicly targeting groups or individuals. In response to these challenges, we participated in this shared task. Our approach involved proposing a fine-tuning-based pre-trained transformer model to effectively discern whether a given text contains offensive content that propagates hatred. We conducted comprehensive experiments, exploring various machine learning (LR, SVM, and Ensemble), deep learning (CNN, BiLSTM, CNN+BiLSTM), and transformer-based models (Indic-SBERT, m- BERT, MuRIL, Distil-BERT, XLM-R), adhering to a meticulous fine-tuning methodology. Among the models evaluated, our fine-tuned L3Cube-Indic-Sentence-Similarity- BERT or Indic-SBERT model demonstrated superior performance, achieving a macro-average F1-score of 0.7013. This notable result positioned us at the 6th place in the task. The implementation details of the task will be found in the GitHub repository.</abstract>
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%0 Conference Proceedings
%T CUET_Binary_Hackers@DravidianLangTech EACL2024: Hate and Offensive Language Detection in Telugu Code-Mixed Text Using Sentence Similarity BERT
%A Farsi, Salman
%A Eusha, Asrarul
%A Hossain, Jawad
%A Ahsan, Shawly
%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 farsi-etal-2024-cuet-binary-hackers
%X With the continuous evolution of technology and widespread internet access, various social media platforms have gained immense popularity, attracting a vast number of active users globally. However, this surge in online activity has also led to a concerning trend by driving many individuals to resort to posting hateful and offensive comments or posts, publicly targeting groups or individuals. In response to these challenges, we participated in this shared task. Our approach involved proposing a fine-tuning-based pre-trained transformer model to effectively discern whether a given text contains offensive content that propagates hatred. We conducted comprehensive experiments, exploring various machine learning (LR, SVM, and Ensemble), deep learning (CNN, BiLSTM, CNN+BiLSTM), and transformer-based models (Indic-SBERT, m- BERT, MuRIL, Distil-BERT, XLM-R), adhering to a meticulous fine-tuning methodology. Among the models evaluated, our fine-tuned L3Cube-Indic-Sentence-Similarity- BERT or Indic-SBERT model demonstrated superior performance, achieving a macro-average F1-score of 0.7013. This notable result positioned us at the 6th place in the task. The implementation details of the task will be found in the GitHub repository.
%U https://aclanthology.org/2024.dravidianlangtech-1.32
%P 193-199
Markdown (Informal)
[CUET_Binary_Hackers@DravidianLangTech EACL2024: Hate and Offensive Language Detection in Telugu Code-Mixed Text Using Sentence Similarity BERT](https://aclanthology.org/2024.dravidianlangtech-1.32) (Farsi et al., DravidianLangTech-WS 2024)
ACL
- Salman Farsi, Asrarul Eusha, Jawad Hossain, Shawly Ahsan, Avishek Das, and Mohammed Moshiul Hoque. 2024. CUET_Binary_Hackers@DravidianLangTech EACL2024: Hate and Offensive Language Detection in Telugu Code-Mixed Text Using Sentence Similarity BERT. In Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 193–199, St. Julian's, Malta. Association for Computational Linguistics.