  In the current era of the internet, where social media platforms are easily
accessible for everyone, people often have to deal with threats, identity
attacks, hate, and bullying due to their association with a cast, creed,
gender, religion, or even acceptance or rejection of a notion. Existing works
in hate speech detection primarily focus on individual comment classification
as a sequence labeling task and often fail to consider the context of the
conversation. The context of a conversation often plays a substantial role when
determining the author's intent and sentiment behind the tweet. This paper
describes the system proposed by team MIDAS-IIITD for HASOC 2021 subtask 2, one
of the first shared tasks focusing on detecting hate speech from Hindi-English
code-mixed conversations on Twitter. We approach this problem using neural
networks, leveraging the transformer's cross-lingual embeddings and further
finetuning them for low-resource hate-speech classification in transliterated
Hindi text. Our best performing system, a hard voting ensemble of Indic-BERT,
XLM-RoBERTa, and Multilingual BERT, achieved a macro F1 score of 0.7253,
placing us first on the overall leaderboard standings.
