# Rank over Class: The Untapped Potential of Ranking in Natural Language Processing

arXiv: https://arxiv.org/abs/2009.05160 · HTML: https://mlsanity.com/paper/2009.05160/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [Transforming Fake News: Robust Generalisable News Classification Using Transformers](https://mlsanity.com/paper/2109.09796/) (2021)
- [Prefer to Classify: Improving Text Classifiers via Auxiliary Preference Learning](https://mlsanity.com/paper/2306.04925/) (2023)
- ["Just Drive": Colour Bias Mitigation for Semantic Segmentation in the Context of Urban Driving](https://mlsanity.com/paper/2112.01121/) (2021)
- [ClaHF: A Human Feedback-inspired Reinforcement Learning Framework for Improving Classification Tasks](https://mlsanity.com/paper/2605.17458/) (2026)

## Abstract (arXiv)

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.