A Semi-Supervised Learning Investigation Under Mobile Text Translation: Based on Graph Convolutional Networks (GCN)

This paper explores using semi-supervised learning and Graph Convolutional Networks (GCNs) in mobile text translation. It delves into the challenges of mobile translation, including language diversity and computational efficiency. The principles of semi-supervised learning and the features of GCN are discussed. Solutions like data augmentation, model compression, and privacy strategies are proposed to address practical challenges like data labeling complexity, computational constraints, and multilingual differences. These techniques have shown promise in enhancing translation quality and reducing labeled data needs. Further research into integrating these methods in mobile translation is recommended, given their impact.