  With the growing use of social media and its availability, many instances of
the use of offensive language have been observed across multiple languages and
domains. This phenomenon has given rise to the growing need to detect the
offensive language used in social media cross-lingually. In OffensEval 2020,
the organizers have released the \textit{multilingual Offensive Language
Identification Dataset} (mOLID), which contains tweets in five different
languages, to detect offensive language. In this work, we introduce a
cross-lingual inductive approach to identify the offensive language in tweets
using the contextual word embedding \textit{XLM-RoBERTa} (XLM-R). We show that
our model performs competitively on all five languages, obtaining the fourth
position in the English task with an F1-score of $0.919$ and eighth position in
the Turkish task with an F1-score of $0.781$. Further experimentation proves
that our model works competitively in a zero-shot learning environment, and is
extensible to other languages.
