A systematic comparison of methods for low-resource dependency parsingon genuinely low-resource languages
Table 1: Train/dev split used for each treebank.
['Language', 'Treebank ID', 'train', 'dev.', 'test']
We use Universal Dependencies (UD) treebanks version 2.2 Nivre et al. Having large development sets allow us to perform better analysis for this study. Now we turn to two truly low-resource treebanks: Galician and Kazakh. These treebanks are most analogous to the North Sami T10 setting and therefore we apply the best approach, cross-lingual training with Morph augmentation. For Galician, we use the Portuguese treebank as source while for Kazakh we use Turkish. Portuguese and Galician have high vocabulary overlap; 62.9% of Galician tokens appear in Portuguese data, while for Turkish and Kazakh they do not share vocabulary since they use different writing systems. However, after transliterating them into the same basic Latin alphabet, we observe that 9.5% of Kazakh tokens appear in the Turkish data. Both language pairs also share many (token-level) character trigrams: 96.0% for Galician-Portuguese and 66.3% for transliterated Kazakh-Turkish.