  With proliferation of user generated contents in social media platforms,
establishing mechanisms to automatically identify toxic and abusive content
becomes a prime concern for regulators, researchers, and society. Keeping the
balance between freedom of speech and respecting each other dignity is a major
concern of social media platform regulators. Although, automatic detection of
offensive content using deep learning approaches seems to provide encouraging
results, training deep learning-based models requires large amounts of
high-quality labeled data, which is often missing. In this regard, we present
in this paper a new deep learning-based method that fuses a Back Translation
method, and a Paraphrasing technique for data augmentation. Our pipeline
investigates different word-embedding-based architectures for classification of
hate speech. The back translation technique relies on an encoder-decoder
architecture pre-trained on a large corpus and mostly used for machine
translation. In addition, paraphrasing exploits the transformer model and the
mixture of experts to generate diverse paraphrases. Finally, LSTM, and CNN are
compared to seek enhanced classification results. We evaluate our proposal on
five publicly available datasets; namely, AskFm corpus, Formspring dataset,
Warner and Waseem dataset, Olid, and Wikipedia toxic comments dataset. The
performance of the proposal together with comparison to some related
state-of-art results demonstrate the effectiveness and soundness of our
proposal.
