  With the exponential rise in user-generated web content on social media, the
proliferation of abusive languages towards an individual or a group across the
different sections of the internet is also rapidly increasing. It is very
challenging for human moderators to identify the offensive contents and filter
those out. Deep neural networks have shown promise with reasonable accuracy for
hate speech detection and allied applications. However, the classifiers are
heavily dependent on the size and quality of the training data. Such a
high-quality large data set is not easy to obtain. Moreover, the existing data
sets that have emerged in recent times are not created following the same
annotation guidelines and are often concerned with different types and
sub-types related to hate. To solve this data sparsity problem, and to obtain
more global representative features, we propose a Convolution Neural Network
(CNN) based multi-task learning models (MTLs)\footnote{code is available at
https://github.com/imprasshant/STL-MTL} to leverage information from multiple
sources. Empirical analysis performed on three benchmark datasets shows the
efficacy of the proposed approach with the significant improvement in accuracy
and F-score to obtain state-of-the-art performance with respect to the existing
systems.
