A systematic comparison of methods for low-resource dependency parsingon genuinely low-resource languages
Table 4: Results for the monolingual POS predictions, ordered by the frequency of each tag in the dev split (%dev). %diff shows the difference between each augmentation method and monolingual models.
['POS', '%dev', 'baseline', '%diff. with +Morph', '%diff. with +Nonce']
We observe that representations generated with monolingual Morph seem to learn better POS, for most of the tags. On the other hand, representations generated with monolingual Nonce sometimes produce lower accuracy on some tags; only on nouns the accuracy is better than monolingual Morph. We hypothesize that this is because Nonce sometimes generates meaningless sentences which confuse the model. In parsing this effect is less apparent, mainly because monolingual Nonce has the poorest POS representation for infrequent tags (%dev), and better representation of nouns.