Revisiting Hierarchical Text Classification: Inference and Metrics

TL;DR

This paper emphasizes the importance of using hierarchical metrics for evaluating hierarchical text classification (HTC), introduces a new dataset, and shows that simple baselines often outperform complex models when properly evaluated.

Contribution

It proposes hierarchical evaluation metrics, introduces a new challenging HTC dataset, and demonstrates the competitiveness of simple baselines over sophisticated models.

Findings

Hierarchical metrics significantly impact model evaluation.

Simple baselines often outperform recent complex models.

Proper evaluation methodology is crucial for HTC research.

Abstract

Hierarchical text classification (HTC) is the task of assigning labels to a text within a structured space organized as a hierarchy. Recent works treat HTC as a conventional multilabel classification problem, therefore evaluating it as such. We instead propose to evaluate models based on specifically designed hierarchical metrics and we demonstrate the intricacy of metric choice and prediction inference method. We introduce a new challenging dataset and we evaluate fairly, recent sophisticated models, comparing them with a range of simple but strong baselines, including a new theoretically motivated loss. Finally, we show that those baselines are very often competitive with the latest models. This highlights the importance of carefully considering the evaluation methodology when proposing new methods for HTC. Code implementation and dataset are available at \url{https://github.com/RomanPlaud/revisitingHTC}.