A systematic comparison of methods for low-resource dependency parsingon genuinely low-resource languages
Table 8: Comparison to CoNLL 2018 UD Shared Task on test sets. best system is the state-of-the-art model for each treebank: UDPipe-Future (Straka, 2018) for Galician and Uppsala (Smith et al., 2018) for Kazakh. rank shows our best model position in the shared task ranking for each treebank.
['[EMPTY]', 'baseline', 'best system', 'cross-lingual +fastText', 'cross-lingual +Morph', 'rank']
For each treebank, we apply the same sentence segmentation and tokenization used by each best system. We see that our approach outperforms the baseline models on both languages. For Kazakh, our model (with transliteration) achieves a competitive LAS (28.23), which would be the second position in the shared task ranking. As comparison, the best system for Kazakh ( Smith et al., Their system use predicted POS as input, while ours depends solely on words and characters. The use of more treebanks and predicted POS is beyond the scope of our paper, but it is interesting that our approach can achieve the second best with such minimal resources. For Galician, our best approach outperforms baseline by 8.09 LAS points. Note that, Galician treebank does not come with training data. Since we treat Galician as our test data, we did not tune on the proportion for training data, but we guess that this is the main reason why our system achieve rank 10 out of 27.