  We describe our top-team solution to Task 1 for Hindi in the HASOC contest
organised by FIRE 2019. The task is to identify hate speech and offensive
language in Hindi. More specifically, it is a binary classification problem
where a system is required to classify tweets into two classes: (a) \emph{Hate
and Offensive (HOF)} and (b) \emph{Not Hate or Offensive (NOT)}. In contrast to
the popular idea of pretraining word vectors (a.k.a. word embedding) with a
large corpus from a general domain such as Wikipedia, we used a relatively
small collection of relevant tweets (i.e. random and sarcasm tweets in Hindi
and Hinglish) for pretraining. We trained a Convolutional Neural Network (CNN)
on top of the pretrained word vectors. This approach allowed us to be ranked
first for this task out of all teams. Our approach could easily be adapted to
other applications where the goal is to predict class of a text when the
provided context is limited.
