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

TL;DR

This paper introduces a novel end-to-end ranking approach using Transformers for NLP tasks, demonstrating that framing problems as ranking rather than classification can lead to significant performance improvements.

Contribution

The paper proposes a new ranking-based method for NLP tasks, challenging the dominance of classification and showing its effectiveness through extensive experiments.

Findings

Ranking approach outperforms traditional classification in sentiment analysis.

Converting ranking results to classification labels improves accuracy by approximately 22%.

The method is effective across various publicly-available datasets.

Abstract

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.