  We compare two orthogonal semi-supervised learning techniques, namely
tri-training and pretrained word embeddings, in the task of dependency parsing.
We explore language-specific FastText and ELMo embeddings and multilingual BERT
embeddings. We focus on a low resource scenario as semi-supervised learning can
be expected to have the most impact here. Based on treebank size and available
ELMo models, we select Hungarian, Uyghur (a zero-shot language for mBERT) and
Vietnamese. Furthermore, we include English in a simulated low-resource
setting. We find that pretrained word embeddings make more effective use of
unlabelled data than tri-training but that the two approaches can be
successfully combined.
