GJG@TamilNLP-ACL2022: Using Transformers for Abusive Comment Classification in Tamil

Abstract

This paper presents transformer-based models for the "Abusive Comment Detection" shared task at the Second Workshop on Speech and Language Technologies for Dravidian Languages at ACL 2022. Our team participated in both the multi-class classification sub-tasks as a part of this shared task. The dataset for sub-task A was in Tamil text; while B was codemixed Tamil-English text. Both the datasets contained 8 classes of abusive comments. We trained an XLM-RoBERTa and DeBERTA base model on the training splits for each subtask. For sub-task A, the XLM-RoBERTa model achieved an accuracy of 0.66 and the DeBERTa model achieved an accuracy of 0.62. For sub-task B, both the models achieved a classification accuracy of 0.72; however, the DeBERTa model performed better in other classification metrics. Our team ranked 2 nd in the code-mixed classification sub-task and 8 th in Tamil-text sub-task.