A systematic comparison of methods for low-resource dependency parsingon genuinely low-resource languages
Table 7: LAS results on development sets. zero-shot denotes results where we predict using model trained only on the source treebank.
['Language', 'zero-shot', 'cross-lingual +fastText', 'cross-lingual +Morph']
Morph augmentation improves performance over the zero-shot baseline and achieves comparable or better LAS with a cross-lingual model trained with pre-trained word embeddings. Next, we look at the effects of transliteration (see Kazakh vs Kazakh (translit.) In the zero-shot experiments, simply mapping both Turkish and Kazakh characters to the Latin alphabet improves accuracy from 12.5 to 21.2 LAS. Cross-lingual training with Morph further improves performance to 36.7 LAS.