Summary of Revisiting Hierarchical Text Classification: Inference and Metrics, by Roman Plaud et al.
Revisiting Hierarchical Text Classification: Inference and Metrics
by Roman Plaud, Matthieu Labeau, Antoine Saillenfest, Thomas Bonald
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a new approach to evaluating hierarchical text classification (HTC) models, which is crucial for assigning labels to texts within structured hierarchies. Instead of treating HTC as a conventional multilabel classification problem, the authors suggest using specifically designed hierarchical metrics to evaluate models. The authors introduce a new challenging dataset and fairly evaluate recent sophisticated models against a range of simple but strong baselines, including a new theoretically motivated loss. Surprisingly, these baselines are often competitive with the latest models, emphasizing the importance of carefully considering evaluation methodology when proposing new methods for HTC. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about how we should measure how good our computer programs are at sorting texts into categories based on their meaning. Right now, people are using a method that’s not really suited to this task. The authors suggest a better way to evaluate these programs and test them out on some new data they’ve created. They also compare the new approach with simpler methods that surprisingly work just as well! This shows us that we need to think carefully about how we measure success when trying to improve these text-sorting programs. |
Keywords
» Artificial intelligence » Classification » Text classification
