MAGE: Multi-Head Attention Guided Embeddings for Low Resource Sentiment Classification
This addresses data scarcity for Bantu language processing, but it appears incremental as it builds on existing augmentation and attention methods.
The paper tackles sentiment classification for low-resource Bantu languages by combining Language-Independent Data Augmentation with Multi-Head Attention weighted embeddings, resulting in improved text classification performance.
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.