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

**Authors:** Amir Atapour-Abarghouei, Stephen Bonner, Andrew Stephen McGough  
**arXiv:** [2009.05160](https://arxiv.org/abs/2009.05160)  
**Date:** 2020-09-10  
**Summary page:** https://www.summarizepaper.com/en/paper/2009.05160v1/

## Key points

- Authors propose a novel approach using text ranking techniques as an alternative to traditional text classification methods
- Challenges of traditional text classification include dataset imbalance, text ambiguity, subjectivity, and lack of linguistic context
- End-to-end ranking approach leverages Transformer networks to generate representations for pairs of text sequences
- Context aggregating network produces ranking scores to establish relevance and ordering of sequences
- Experiment on sentiment analysis dataset showed a significant 22% improvement over state-of-the-art text classification methods when converting ranking results into classification labels
- Study highlights the potential benefits of incorporating ranking methodologies in natural language processing tasks for improved performance and accuracy

## Summary

In their paper titled "Rank over Class: The Untapped Potential of Ranking in Natural Language Processing," authors Amir Atapour-Abarghouei, Stephen Bonner, and Andrew Stephen McGough delve into the limitations of traditional text classification methods and propose a novel approach using text ranking techniques. Text classification has been widely used in various applications such as sentiment analysis, content tagging, recommender systems, and spam detection. However, it faces challenges due to dataset imbalance, text ambiguity, subjectivity, and the lack of linguistic context. The authors introduce an end-to-end ranking approach that leverages Transformer networks to generate representations for pairs of text sequences. These representations are then fed into a context aggregating network that produces ranking scores to establish the relevance and ordering of the sequences. Through experiments on publicly-available datasets, they explore the applicability of their ranking approach to tasks typically addressed through classification. One notable experiment involved a heavily-skewed sentiment analysis dataset where converting ranking results into classification labels led to a significant 22% improvement over state-of-the-art text classification methods. This demonstrates the effectiveness of text ranking over traditional classification approaches in certain scenarios. The study sheds light on the untapped potential of incorporating ranking methodologies in natural language processing tasks, offering new insights and avenues for improving performance and accuracy in challenging classification-based problems.