  Scarcity of domain-specific data in the Portuguese financial domain has
disfavored the development of Natural Language Processing (NLP) applications.
To address this limitation, the present study advocates for the utilization of
synthetic data generated through data augmentation techniques. The
investigation focuses on the augmentation of a dataset sourced from the Central
Bank of Brazil FAQ, employing techniques that vary in semantic similarity.
Supervised and unsupervised tasks are conducted to evaluate the impact of
augmented data on both low and high semantic similarity scenarios.
Additionally, the resultant dataset will be publicly disseminated on the
Hugging Face Datasets platform, thereby enhancing accessibility and fostering
broader engagement within the NLP research community.
