  Cross-lingual text classification aims at training a classifier on the source
language and transferring the knowledge to target languages, which is very
useful for low-resource languages. Recent multilingual pretrained language
models (mPLM) achieve impressive results in cross-lingual classification tasks,
but rarely consider factors beyond semantic similarity, causing performance
degradation between some language pairs. In this paper we propose a simple yet
effective method to incorporate heterogeneous information within and across
languages for cross-lingual text classification using graph convolutional
networks (GCN). In particular, we construct a heterogeneous graph by treating
documents and words as nodes, and linking nodes with different relations, which
include part-of-speech roles, semantic similarity, and document translations.
Extensive experiments show that our graph-based method significantly
outperforms state-of-the-art models on all tasks, and also achieves consistent
performance gain over baselines in low-resource settings where external tools
like translators are unavailable.
