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The internet today has become an unrivalled source of information wherepeople converse on content based websites such as Quora, Reddit, StackOverflowand Twitter asking doubts and sharing knowledge with the world. A major arisingproblem with such websites is the proliferation of toxic comments or instancesof insincerity wherein the users instead of maintaining a sincere motiveindulge in spreading toxic and divisive content. The straightforward course ofaction in confronting this situation is detecting such content beforehand andpreventing it from subsisting online. In recent times Transfer Learning inNatural Language Processing has seen an unprecedented growth. Today with theexistence of transformers and various state of the art innovations, atremendous growth has been made in various NLP domains. The introduction ofBERT has caused quite a stir in the NLP community. As mentioned, whenpublished, BERT dominated performance benchmarks and thereby inspired manyother authors to experiment with it and publish similar models. This led to thedevelopment of a whole BERT-family, each member being specialized on adifferent task. In this paper we solve the Insincere Questions Classificationproblem by fine tuning four cutting age models viz BERT, RoBERTa, DistilBERTand ALBERT.

Detecting Insincere Questions from Text: A Transfer Learning Approach