# Performance of Data Augmentation Methods for Brazilian Portuguese Text Classification

arXiv: https://arxiv.org/abs/2304.02785 · HTML: https://mlsanity.com/paper/2304.02785/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 5 Apr 2023.

## Builds on (most cited first, 10 of 10)

- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization](https://mlsanity.com/paper/1911.03437/) (2019)
- [Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations](https://mlsanity.com/paper/1805.06201/) (2018)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [Portuguese Named Entity Recognition using BERT-CRF](https://mlsanity.com/paper/1909.10649/) (2019)
- [Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding](https://mlsanity.com/paper/2007.09536/) (2020)
- [Building a Sentiment Corpus of Tweets in Brazilian Portuguese](https://mlsanity.com/paper/1712.08917/) (2017)
- [Learning document embeddings along with their uncertainties](https://mlsanity.com/paper/1908.07599/) (2019)

## Abstract (arXiv)

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.