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.
@article{arxiv.2204.09675,
title = {Optimize_Prime@DravidianLangTech-ACL2022: Abusive Comment Detection in Tamil},
author = {Shantanu Patankar and Omkar Gokhale and Onkar Litake and Aditya Mandke and Dipali Kadam},
journal= {arXiv preprint arXiv:2204.09675},
year = {2022}
}
Comments
arXiv admin note: text overlap with arXiv:2204.09087