  Over the past decade, we have seen exponential growth in online content
fueled by social media platforms. Data generation of this scale comes with the
caveat of insurmountable offensive content in it. The complexity of identifying
offensive content is exacerbated by the usage of multiple modalities (image,
language, etc.), code-mixed language and more. Moreover, even after careful
sampling and annotation of offensive content, there will always exist a
significant class imbalance between offensive and non-offensive content. In
this paper, we introduce a novel Code-Mixing Index (CMI) based focal loss which
circumvents two challenges (1) code-mixing in languages (2) class imbalance
problem for Dravidian language offense detection. We also replace the
conventional dot product-based classifier with the cosine-based classifier
which results in a boost in performance. Further, we use multilingual models
that help transfer characteristics learnt across languages to work effectively
with low resourced languages. It is also important to note that our model
handles instances of mixed script (say usage of Latin and Dravidian-Tamil
script) as well. To summarize, our model can handle offensive language
detection in a low-resource, class imbalanced, multilingual and code-mixed
setting.
