  We present FewShotTextGCN, a novel method designed to effectively utilize the
properties of word-document graphs for improved learning in low-resource
settings. We introduce K-hop Neighbourhood Regularization, a regularizer for
heterogeneous graphs, and show that it stabilizes and improves learning when
only a few training samples are available. We furthermore propose a
simplification in the graph-construction method, which results in a graph that
is $\sim$7 times less dense and yields better performance in little-resource
settings while performing on par with the state of the art in high-resource
settings. Finally, we introduce a new variant of Adaptive Pseudo-Labeling
tailored for word-document graphs. When using as little as 20 samples for
training, we outperform a strong TextGCN baseline with 17% in absolute accuracy
on average over eight languages. We demonstrate that our method can be applied
to document classification without any language model pretraining on a wide
range of typologically diverse languages while performing on par with large
pretrained language models.
