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Hate Speech has become a major content moderation issue for online socialmedia platforms. Given the volume and velocity of online content production, itis impossible to manually moderate hate speech related content on any platform.In this paper we utilize a multi-task and multi-lingual approach based onrecently proposed Transformer Neural Networks to solve three sub-tasks for hatespeech. These sub-tasks were part of the 2019 shared task on hate speech andoffensive content (HASOC) identification in Indo-European languages. We expandon our submission to that competition by utilizing multi-task models which aretrained using three approaches, a) multi-task learning with separate taskheads, b) back-translation, and c) multi-lingual training. Finally, weinvestigate the performance of various models and identify instances where theTransformer based models perform differently and better. We show that it ispossible to to utilize different combined approaches to obtain models that cangeneralize easily on different languages and tasks, while trading off slightaccuracy (in some cases) for a much reduced inference time compute cost. Weopen source an updated version of our HASOC 2019 code with the new improvementsat https://github.com/socialmediaie/MTML_HateSpeech.

Exploring multi-task multi-lingual learning of transformer models for hate speech and offensive speech identification in social media