  On current {\it e-}learning platforms, live classes are an important tool
that provides students with an opportunity to get more involved while learning
new concepts. In such classes, the element of interaction with teachers and
fellow peers helps in removing learning silos and gives each student a chance
to experience some aspects relevant to offline learning in this era of virtual
classes. One common way of interaction in a class is through the chats /
messaging framework, where the teacher can broadcast messages as well as get
instant feedback from the students in the live class. This freedom of
interaction is a crucial aspect for any student's learning growth but misuse of
it can have serious repercussions. Some miscreants use this framework to send
profane messages which can have a negative impact on other students as well as
the teacher of the class. These rare but high impact situations obviate the
need for automatic detection mechanisms that prevent the posting of such chats
on any platform. In this work we develop YZR-Net which is a self-supervised
framework that is able to robustly detect profane words used in a chat even if
the student tries to add clever modifications to fool the system. The matching
mechanism on token / word level allows us to maintain a compact as well as
dynamic profane vocabulary which can be updated without retraining the
underlying model. Our profanity detection framework is language independent and
can handle abuses in both English as well as its transliterated counterpart
Hinglish (Hindi language words written in English).
