Name
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. |
Year | Venue | DocType |
|---|---|---|
2021 | EMNLP | Conference |
Volume | Citations | PageRank |
2021.emnlp-main | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joachim Wagner | 1 | 103 | 7.74 |
| jennifer foster | 2 | 454 | 38.25 |