MAGE：用于低资源情感分类的多头注意力引导嵌入
MAGE: Multi-Head Attention Guided Embeddings for Low Resource Sentiment Classification
AI总结：
针对低资源班图语言数据稀缺问题，提出结合语言无关数据增强与多头注意力加权嵌入的模型，以增强关键数据点，提升文本分类性能，为低资源语言处理奠定基础。
摘要：
由于低资源班图语言缺乏高质量数据，文本分类及其他实际应用面临重大挑战。本文提出了一种先进模型，将语言无关数据增强（LiDA）与基于多头注意力的加权嵌入相结合，以选择性地增强关键数据点并提升文本分类性能。这种整合使我们能够创建在各种语言环境中均有效的稳健数据增强策略，确保模型能够处理班图语言独特的句法和语义特征。该方法不仅解决了数据稀缺问题，还为低资源语言处理与分类任务的未来研究奠定了基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
