Optimize_Prime@DravidianLangTech-ACL2022: Abusive Comment Detection in Tamil
Shantanu Patankar, Omkar Gokhale, Onkar Litake, Aditya Mandke, Dipali Kadam · 2022
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 Codemixed data, MuRIL and M-BERT provided sublime results, with a macro-averaged f1 score of 0.45.