  Offensive Language detection in social media platforms has been an active
field of research over the past years. In non-native English spoken countries,
social media users mostly use a code-mixed form of text in their
posts/comments. This poses several challenges in the offensive content
identification tasks, and considering the low resources available for Tamil,
the task becomes much harder. The current study presents extensive experiments
using multiple deep learning, and transfer learning models to detect offensive
content on YouTube. We propose a novel and flexible approach of selective
translation and transliteration techniques to reap better results from
fine-tuning and ensembling multilingual transformer networks like BERT, Distil-
BERT, and XLM-RoBERTa. The experimental results showed that ULMFiT is the best
model for this task. The best performing models were ULMFiT and mBERTBiLSTM for
this Tamil code-mix dataset instead of more popular transfer learning models
such as Distil- BERT and XLM-RoBERTa and hybrid deep learning models. The
proposed model ULMFiT and mBERTBiLSTM yielded good results and are promising
for effective offensive speech identification in low-resourced languages.
