  This work empirically demonstrates the ability of Text Graph Convolutional
Network (Text GCN) to outperform traditional natural language processing
benchmarks for the task of semi-supervised Swahili news classification. In
particular, we focus our experimentation on the sparsely-labelled
semi-supervised context which is representative of the practical constraints
facing low-resourced African languages. We follow up on this result by
introducing a variant of the Text GCN model which utilises a bag of words
embedding rather than a naive one-hot encoding to reduce the memory footprint
of Text GCN whilst demonstrating similar predictive performance.
