Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification
Yi Zhu, Ehsan Shareghi, Yingzhen Li, Roi Reichart, Anna Korhonen · 2021
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 lowresource settings across several languages.1