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arXiv · 2109.08122

Revisiting Tri-training of Dependency Parsers

Joachim Wagner·Jennifer Foster
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Abstract

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.

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Joachim Wagner, Jennifer Foster. 2021-09-16. Revisiting Tri-training of Dependency Parsers. https://doi.org/10.18653/v1/2021.emnlp-main.745

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