Recently, Automated Machine Learning (AutoML) has registered increasing success with respect to tabular data. However, the question arises whether AutoML can also be applied effectively to text classification tasks. This work compares four AutoML tools on 13 different popular datasets, including Kaggle competitions, and opposes human performance. The results show that the AutoML tools perform better than the machine learning community in 4 out of 13 tasks and that two stand out.
@article{arxiv.2012.03575,
title = {Leveraging Automated Machine Learning for Text Classification: Evaluation of AutoML Tools and Comparison with Human Performance},
author = {Matthias Blohm and Marc Hanussek and Maximilien Kintz},
journal= {arXiv preprint arXiv:2012.03575},
year = {2020}
}
Comments
Accepted for ICAART 2021 conference as a Short Paper