We select several languages from each language family, and investigate the effect of the amount of training data on the performances of zero-shot cross-lingual dependency parsing.
we take the SVD-based approach, since it is faster than the GD-based one, and trained different transformation models with different amount of parallel sentences from Europarl dataset on each of the 13 selected languages.