论文标题
利用自动化的机器学习进行文本分类：评估汽车工具并与人类绩效进行比较
Leveraging Automated Machine Learning for Text Classification: Evaluation of AutoML Tools and Comparison with Human Performance
论文作者
论文摘要
最近，自动化机器学习（AUTOML）在表格数据方面已注册了越来越多的成功。但是，出现的问题是是否也可以有效地应用于文本分类任务。这项工作比较了13个不同流行数据集（包括Kaggle竞赛）上的四种汽车工具，并反对人类绩效。结果表明，在13个任务中有4个任务中，汽车工具的性能比机器学习社区的表现更好，这两个脱颖而出。
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.
