  Improving machine learning performance while increasing model generalization
has been a constantly pursued goal by AI researchers. Data augmentation
techniques are often used towards achieving this target, and most of its
evaluation is made using English corpora. In this work, we took advantage of
different existing data augmentation methods to analyze their performances
applied to text classification problems using Brazilian Portuguese corpora. As
a result, our analysis shows some putative improvements in using some of these
techniques; however, it also suggests further exploitation of language bias and
non-English text data scarcity.
