Optimize_Prime@DravidianLangTech-ACL2022: Abusive Comment Detection in Tamil

• URL: http://arxiv.org/abs/2204.09675v1
• Date: Tue, 19 Apr 2022 18:55:18 GMT
• Title: Optimize_Prime@DravidianLangTech-ACL2022: Abusive Comment Detection in Tamil
• Authors: Shantanu Patankar, Omkar Gokhale, Onkar Litake, Aditya Mandke, Dipali Kadam
• Abstract summary: This paper tries to address the problem of abusive comment detection in low-resource indic languages. This task detects and classifies YouTube comments in Tamil and Tamil- English Codemixed format into multiple categories.
• Score: 1.0066310107046081
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 sub-lime results, with a macro-averaged f1 score of 0.45.

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