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This paper is a contribution to the Hate Speech and Offensive ContentIdentification in Indo-European Languages (HASOC) 2021 shared task. Socialmedia today is a hotbed of toxic and hateful conversations, in variouslanguages. Recent news reports have shown that current models struggle toautomatically identify hate posted in minority languages. Therefore,efficiently curbing hate speech is a critical challenge and problem ofinterest. We present a multilingual architecture using state-of-the-arttransformer language models to jointly learn hate and offensive speechdetection across three languages namely, English, Hindi, and Marathi. On theprovided testing corpora, we achieve Macro F1 scores of 0.7996, 0.7748, 0.8651for sub-task 1A and 0.6268, 0.5603 during the fine-grained classification ofsub-task 1B. These results show the efficacy of exploiting a multilingualtraining scheme.

One to rule them all: Towards Joint Indic Language Hate Speech Detection