  The presence of offensive language on social media is very common motivating
platforms to invest in strategies to make communities safer. This includes
developing robust machine learning systems capable of recognizing offensive
content online. Apart from a few notable exceptions, most research on automatic
offensive language identification has dealt with English and a few other high
resource languages such as French, German, and Spanish. In this paper we
address this gap by tackling offensive language identification in Marathi, a
low-resource Indo-Aryan language spoken in India. We introduce the Marathi
Offensive Language Dataset v.2.0 or MOLD 2.0 and present multiple experiments
on this dataset. MOLD 2.0 is a much larger version of MOLD with expanded
annotation to the levels B (type) and C (target) of the popular OLID taxonomy.
MOLD 2.0 is the first hierarchical offensive language dataset compiled for
Marathi, thus opening new avenues for research in low-resource Indo-Aryan
languages. Finally, we also introduce SeMOLD, a larger dataset annotated
following the semi-supervised methods presented in SOLID.
