Optimize_Prime@DravidianLangTech-ACL2022: Abusive Comment Detection in Tamil
This addresses the problem of detecting abusive content on social media for Tamil speakers, but it is incremental as it applies existing methods to a specific shared task.
The paper tackled abusive comment detection in low-resource Tamil and Tamil-English code-mixed languages, achieving macro-averaged F1 scores of 0.43 for Tamil and 0.45 for code-mixed data using models like MuRIL and XLM-RoBERTa.
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.