Abstract
Due to the lack of quality data for low-resource Bantu languages, significant challenges are presented in text classification and other practical implementations. In this paper, we introduce an advanced model combining Language-Independent Data Augmentation (LiDA) with Multi-Head Attention based weighted embeddings to selectively enhance critical data points and improve text classification performance. This integration allows us to create robust data augmentation strategies that are effective across various linguistic contexts, ensuring that our model can handle the unique syntactic and semantic features of Bantu languages. This approach not only addresses the data scarcity issue but also sets a foundation for future research in low-resource language processing and classification tasks.
Abstract (translated)
由于缺乏高质量的数据，低资源班图语的语言文本分类和其他实际应用面临重大挑战。在本文中，我们引入了一种结合语言无关数据增强（LiDA）与基于多头注意力机制的加权嵌入的高级模型。这种组合允许我们选择性地强化关键数据点，并提高文本分类性能。该集成使得我们可以创建有效的数据增强策略，适用于各种语言环境，确保我们的模型能够处理班图语独特的语法和语义特征。这一方法不仅解决了数据稀缺的问题，还为低资源语言处理和分类任务的未来研究奠定了基础。
URL
https://arxiv.org/abs/2502.17987
