Comparing BERT against traditional machine learning text classification

TL;DR

This paper empirically compares BERT with traditional machine learning methods like TF-IDF and classical NLP approaches, demonstrating BERT's superior performance and robustness across various NLP classification tasks.

Contribution

It provides empirical evidence supporting BERT as a default choice for NLP classification tasks over traditional methods.

Findings

BERT outperforms traditional TF-IDF-based classifiers.

BERT's performance is consistent across different languages.

Empirical support for adopting BERT as a standard NLP tool.

Abstract

The BERT model has arisen as a popular state-of-the-art machine learning model in the recent years that is able to cope with multiple NLP tasks such as supervised text classification without human supervision. Its flexibility to cope with any type of corpus delivering great results has make this approach very popular not only in academia but also in the industry. Although, there are lots of different approaches that have been used throughout the years with success. In this work, we first present BERT and include a little review on classical NLP approaches. Then, we empirically test with a suite of experiments dealing different scenarios the behaviour of BERT against the traditional TF-IDF vocabulary fed to machine learning algorithms. Our purpose of this work is to add empirical evidence to support or refuse the use of BERT as a default on NLP tasks. Experiments show the superiority of BERT and its independence of features of the NLP problem such as the language of the text adding empirical evidence to use BERT as a default technique to be used in NLP problems.