  Text classification has long been a staple within Natural Language Processing
(NLP) with applications spanning across diverse areas such as sentiment
analysis, recommender systems and spam detection. With such a powerful
solution, it is often tempting to use it as the go-to tool for all NLP problems
since when you are holding a hammer, everything looks like a nail. However, we
argue here that many tasks which are currently addressed using classification
are in fact being shoehorned into a classification mould and that if we instead
address them as a ranking problem, we not only improve the model, but we
achieve better performance. We propose a novel end-to-end ranking approach
consisting of a Transformer network responsible for producing representations
for a pair of text sequences, which are in turn passed into a context
aggregating network outputting ranking scores used to determine an ordering to
the sequences based on some notion of relevance. We perform numerous
experiments on publicly-available datasets and investigate the applications of
ranking in problems often solved using classification. In an experiment on a
heavily-skewed sentiment analysis dataset, converting ranking results to
classification labels yields an approximately 22% improvement over
state-of-the-art text classification, demonstrating the efficacy of text
ranking over text classification in certain scenarios.
