  Due to the wide adoption of social media platforms like Facebook, Twitter,
etc., there is an emerging need of detecting online posts that can go against
the community acceptance standards. The hostility detection task has been well
explored for resource-rich languages like English, but is unexplored for
resource-constrained languages like Hindidue to the unavailability of large
suitable data. We view this hostility detection as a multi-label multi-class
classification problem. We propose an effective neural network-based technique
for hostility detection in Hindi posts. We leverage pre-trained multilingual
Bidirectional Encoder Representations of Transformer (mBERT) to obtain the
contextual representations of Hindi posts. We have performed extensive
experiments including different pre-processing techniques, pre-trained models,
neural architectures, hybrid strategies, etc. Our best performing neural
classifier model includes One-vs-the-Rest approach where we obtained 92.60%,
81.14%,69.59%, 75.29% and 73.01% F1 scores for hostile, fake, hate, offensive,
and defamation labels respectively. The proposed model outperformed the
existing baseline models and emerged as the state-of-the-art model for
detecting hostility in the Hindi posts.
