  Sentiment analysis is the most basic NLP task to determine the polarity of
text data. There has been a significant amount of work in the area of
multilingual text as well. Still hate and offensive speech detection faces a
challenge due to inadequate availability of data, especially for Indian
languages like Hindi and Marathi. In this work, we consider hate and offensive
speech detection in Hindi and Marathi texts. The problem is formulated as a
text classification task using the state of the art deep learning approaches.
We explore different deep learning architectures like CNN, LSTM, and variations
of BERT like multilingual BERT, IndicBERT, and monolingual RoBERTa. The basic
models based on CNN and LSTM are augmented with fast text word embeddings. We
use the HASOC 2021 Hindi and Marathi hate speech datasets to compare these
algorithms. The Marathi dataset consists of binary labels and the Hindi dataset
consists of binary as well as more-fine grained labels. We show that the
transformer-based models perform the best and even the basic models along with
FastText embeddings give a competitive performance. Moreover, with normal
hyper-parameter tuning, the basic models perform better than BERT-based models
on the fine-grained Hindi dataset.
