  Semi-supervised learning through deep generative models and multi-lingual
pretraining techniques have orchestrated tremendous success across different
areas of NLP. Nonetheless, their development has happened in isolation, while
the combination of both could potentially be effective for tackling
task-specific labelled data shortage. To bridge this gap, we combine
semi-supervised deep generative models and multi-lingual pretraining to form a
pipeline for document classification task. Compared to strong supervised
learning baselines, our semi-supervised classification framework is highly
competitive and outperforms the state-of-the-art counterparts in low-resource
settings across several languages.
