  The increasing accessibility of the internet facilitated social media usage
and encouraged individuals to express their opinions liberally. Nevertheless,
it also creates a place for content polluters to disseminate offensive posts or
contents. Most of such offensive posts are written in a cross-lingual manner
and can easily evade the online surveillance systems. This paper presents an
automated system that can identify offensive text from multilingual code-mixed
data. In the task, datasets provided in three languages including Tamil,
Malayalam and Kannada code-mixed with English where participants are asked to
implement separate models for each language. To accomplish the tasks, we
employed two machine learning techniques (LR, SVM), three deep learning (LSTM,
LSTM+Attention) techniques and three transformers (m-BERT, Indic-BERT, XLM-R)
based methods. Results show that XLM-R outperforms other techniques in Tamil
and Malayalam languages while m-BERT achieves the highest score in the Kannada
language. The proposed models gained weighted $f_1$ score of $0.76$ (for
Tamil), $0.93$ (for Malayalam), and $0.71$ (for Kannada) with a rank of
$3^{rd}$, $5^{th}$ and $4^{th}$ respectively.
