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• W3213037944 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." @default.
• W3213037944 created "2021-11-22" @default.
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• W3213037944 date "2021-11-01" @default.
• W3213037944 modified "2023-09-23" @default.
• W3213037944 title "Revisiting Tri-training of Dependency Parsers" @default.
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• W3213037944 hasPublicationYear "2021" @default.
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