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Semi-supervised learning through deep generative models and multi-lingualpretraining techniques have orchestrated tremendous success across differentareas of NLP. Nonetheless, their development has happened in isolation, whilethe combination of both could potentially be effective for tacklingtask-specific labelled data shortage. To bridge this gap, we combinesemi-supervised deep generative models and multi-lingual pretraining to form apipeline for document classification task. Compared to strong supervisedlearning baselines, our semi-supervised classification framework is highlycompetitive and outperforms the state-of-the-art counterparts in low-resourcesettings across several languages.

Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification