  In the recent past, social media platforms have helped people in connecting
and communicating to a wider audience. But this has also led to a drastic
increase in cyberbullying. It is essential to detect and curb hate speech to
keep the sanity of social media platforms. Also, code mixed text containing
more than one language is frequently used on these platforms. We, therefore,
propose automated techniques for hate speech detection in code mixed text from
scraped Twitter. We specifically focus on code mixed English-Hindi text and
transformer-based approaches. While regular approaches analyze the text
independently, we also make use of content text in the form of parent tweets.
We try to evaluate the performances of multilingual BERT and Indic-BERT in
single-encoder and dual-encoder settings. The first approach is to concatenate
the target text and context text using a separator token and get a single
representation from the BERT model. The second approach encodes the two texts
independently using a dual BERT encoder and the corresponding representations
are averaged. We show that the dual-encoder approach using independent
representations yields better performance. We also employ simple ensemble
methods to further improve the performance. Using these methods we report the
best F1 score of 73.07% on the HASOC 2021 ICHCL code mixed data set.
