@inproceedings{biradar-saumya-2022-iiitdwd,
title = "{IIITDWD}@{T}amil{NLP}-{ACL}2022: Transformer-based approach to classify abusive content in {D}ravidian Code-mixed text",
author = "Biradar, Shankar and
Saumya, Sunil",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Krishnamurthy, Parameswari and
Sherly, Elizabeth and
Mahesan, Sinnathamby",
booktitle = "Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.dravidianlangtech-1.16",
doi = "10.18653/v1/2022.dravidianlangtech-1.16",
pages = "100--104",
abstract = "Identifying abusive content or hate speech in social media text has raised the research community{'}s interest in recent times. The major driving force behind this is the widespread use of social media websites. Further, it also leads to identifying abusive content in low-resource regional languages, which is an important research problem in computational linguistics. As part of ACL-2022, organizers of DravidianLangTech@ACL 2022 have released a shared task on abusive category identification in Tamil and Tamil-English code-mixed text to encourage further research on offensive content identification in low-resource Indic languages. This paper presents the working notes for the model submitted by IIITDWD at DravidianLangTech@ACL 2022. Our team competed in Sub-Task B and finished in 9th place among the participating teams. In our proposed approach, we used a pre-trained transformer model such as Indic-bert for feature extraction, and on top of that, SVM classifier is used for stance detection. Further, our model achieved 62 {\%} accuracy on code-mixed Tamil-English text.",
}
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<abstract>Identifying abusive content or hate speech in social media text has raised the research community’s interest in recent times. The major driving force behind this is the widespread use of social media websites. Further, it also leads to identifying abusive content in low-resource regional languages, which is an important research problem in computational linguistics. As part of ACL-2022, organizers of DravidianLangTech@ACL 2022 have released a shared task on abusive category identification in Tamil and Tamil-English code-mixed text to encourage further research on offensive content identification in low-resource Indic languages. This paper presents the working notes for the model submitted by IIITDWD at DravidianLangTech@ACL 2022. Our team competed in Sub-Task B and finished in 9th place among the participating teams. In our proposed approach, we used a pre-trained transformer model such as Indic-bert for feature extraction, and on top of that, SVM classifier is used for stance detection. Further, our model achieved 62 % accuracy on code-mixed Tamil-English text.</abstract>
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%0 Conference Proceedings
%T IIITDWD@TamilNLP-ACL2022: Transformer-based approach to classify abusive content in Dravidian Code-mixed text
%A Biradar, Shankar
%A Saumya, Sunil
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Krishnamurthy, Parameswari
%Y Sherly, Elizabeth
%Y Mahesan, Sinnathamby
%S Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F biradar-saumya-2022-iiitdwd
%X Identifying abusive content or hate speech in social media text has raised the research community’s interest in recent times. The major driving force behind this is the widespread use of social media websites. Further, it also leads to identifying abusive content in low-resource regional languages, which is an important research problem in computational linguistics. As part of ACL-2022, organizers of DravidianLangTech@ACL 2022 have released a shared task on abusive category identification in Tamil and Tamil-English code-mixed text to encourage further research on offensive content identification in low-resource Indic languages. This paper presents the working notes for the model submitted by IIITDWD at DravidianLangTech@ACL 2022. Our team competed in Sub-Task B and finished in 9th place among the participating teams. In our proposed approach, we used a pre-trained transformer model such as Indic-bert for feature extraction, and on top of that, SVM classifier is used for stance detection. Further, our model achieved 62 % accuracy on code-mixed Tamil-English text.
%R 10.18653/v1/2022.dravidianlangtech-1.16
%U https://aclanthology.org/2022.dravidianlangtech-1.16
%U https://doi.org/10.18653/v1/2022.dravidianlangtech-1.16
%P 100-104
Markdown (Informal)
[IIITDWD@TamilNLP-ACL2022: Transformer-based approach to classify abusive content in Dravidian Code-mixed text](https://aclanthology.org/2022.dravidianlangtech-1.16) (Biradar & Saumya, DravidianLangTech 2022)
ACL