Proposal and Linguistic Assessment of Data Augmentation Techniques

• Arthur Scalercio UFF http://orcid.org/0009-0005-1913-840X
• Cláudia Freitas PUC-RJ https://orcid.org/0000-0001-6807-8558

DOI: https://doi.org/10.5753/stil.2023.234185

Abstract

In Natural Language Processing (NLP), data augmentation consists of creating artificial training data for machine learning through textual transformations, aiming to improve the model’s generalization capabilities and its performance in a range of downstream NLP tasks. Most studies on data augmentation techniques are evaluated based on the performance of the trained model with the artificially generated texts directly in the target task, without concern for linguistically evaluating the quality of the created texts. In this study, we propose two data augmentation techniques, evaluate the linguistic quality of the transformed texts, and demonstrate that the texts are well-constructed linguistically.

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