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.

利用数据增强技术生成合成数据以解决葡萄牙金融领域中领域特定数据稀缺的问题，并使用不同语义相似性的技术对从巴西中央银行 FAQ 获取的数据集进行增强，通过有监督和无监督任务评估增强数据对低语义相似性和高语义相似性情况的影响。此外，生成的数据集将在 Hugging Face 数据集平台上公开传播，从而提高可访问性并促进自然语言处理研究社区的更广泛参与。