Detecting Insincere Questions from Text: A Transfer Learning Approach

TL;DR

This paper addresses the detection of insincere questions on online platforms by fine-tuning advanced transformer models like BERT, RoBERTa, DistilBERT, and ALBERT to improve classification accuracy.

Contribution

It introduces a transfer learning approach using multiple state-of-the-art transformer models for insincere question detection, demonstrating their effectiveness.

Findings

Transformer models outperform traditional methods

Fine-tuning BERT-based models improves detection accuracy

ALBERT achieves the best performance among tested models

Abstract

The internet today has become an unrivalled source of information where people converse on content based websites such as Quora, Reddit, StackOverflow and Twitter asking doubts and sharing knowledge with the world. A major arising problem with such websites is the proliferation of toxic comments or instances of insincerity wherein the users instead of maintaining a sincere motive indulge in spreading toxic and divisive content. The straightforward course of action in confronting this situation is detecting such content beforehand and preventing it from subsisting online. In recent times Transfer Learning in Natural Language Processing has seen an unprecedented growth. Today with the existence of transformers and various state of the art innovations, a tremendous growth has been made in various NLP domains. The introduction of BERT has caused quite a stir in the NLP community. As mentioned, when published, BERT dominated performance benchmarks and thereby inspired many other authors to experiment with it and publish similar models. This led to the development of a whole BERT-family, each member being specialized on a different task. In this paper we solve the Insincere Questions Classification problem by fine tuning four cutting age models viz BERT, RoBERTa, DistilBERT and ALBERT.