Optimize_Prime@DravidianLangTech-ACL2022: Abusive Comment Detection in Tamil

Shantanu Patankar, Omkar Gokhale, Onkar Litake, Aditya Mandke, Dipali Kadam


Abstract
This paper tries to address the problem of abusive comment detection in low-resource indic languages. Abusive comments are statements that are offensive to a person or a group of people. These comments are targeted toward individuals belonging to specific ethnicities, genders, caste, race, sexuality, etc. Abusive Comment Detection is a significant problem, especially with the recent rise in social media users. This paper presents the approach used by our team — Optimize_Prime, in the ACL 2022 shared task “Abusive Comment Detection in Tamil.” This task detects and classifies YouTube comments in Tamil and Tamil-English Codemixed format into multiple categories. We have used three methods to optimize our results: Ensemble models, Recurrent Neural Networks, and Transformers. In the Tamil data, MuRIL and XLM-RoBERTA were our best performing models with a macro-averaged f1 score of 0.43. Furthermore, for the Code-mixed data, MuRIL and M-BERT provided sublime results, with a macro-averaged f1 score of 0.45.
Anthology ID:
2022.dravidianlangtech-1.36
Volume:
Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
DravidianLangTech
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
235–239
Language:
URL:
https://aclanthology.org/2022.dravidianlangtech-1.36
DOI:
10.18653/v1/2022.dravidianlangtech-1.36
Bibkey:
Cite (ACL):
Shantanu Patankar, Omkar Gokhale, Onkar Litake, Aditya Mandke, and Dipali Kadam. 2022. Optimize_Prime@DravidianLangTech-ACL2022: Abusive Comment Detection in Tamil. In Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages, pages 235–239, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Optimize_Prime@DravidianLangTech-ACL2022: Abusive Comment Detection in Tamil (Patankar et al., DravidianLangTech 2022)
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