Performance of Data Augmentation Methods for Brazilian Portuguese Text Classification
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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.
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References · 10
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- 2018Learning Word Vectors for 157 Languagescited by 280
- 2019SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimizationcited by 254
- 2018Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relationscited by 201
- 2019Text Classification Algorithms: A Surveycited by 163
- 2019Portuguese Named Entity Recognition using BERT-CRFcited by 73
- 2020Hierarchical Topic Mining via Joint Spherical Tree and Text Embeddingcited by 22
- 2017Building a Sentiment Corpus of Tweets in Brazilian Portuguesecited by 20
- 2019Learning document embeddings along with their uncertaintiescited by 4
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