  Hate Speech has become a major content moderation issue for online social
media platforms. Given the volume and velocity of online content production, it
is impossible to manually moderate hate speech related content on any platform.
In this paper we utilize a multi-task and multi-lingual approach based on
recently proposed Transformer Neural Networks to solve three sub-tasks for hate
speech. These sub-tasks were part of the 2019 shared task on hate speech and
offensive content (HASOC) identification in Indo-European languages. We expand
on our submission to that competition by utilizing multi-task models which are
trained using three approaches, a) multi-task learning with separate task
heads, b) back-translation, and c) multi-lingual training. Finally, we
investigate the performance of various models and identify instances where the
Transformer based models perform differently and better. We show that it is
possible to to utilize different combined approaches to obtain models that can
generalize easily on different languages and tasks, while trading off slight
accuracy (in some cases) for a much reduced inference time compute cost. We
open source an updated version of our HASOC 2019 code with the new improvements
at https://github.com/socialmediaie/MTML_HateSpeech.
