A systematic comparison of methods for low-resource dependency parsingon genuinely low-resource languages
Table 2: Number of North Sámi training sentences.
['[EMPTY]', 'original', '+Morph', '+Nonce']
North Sámi is our largest low-resource treebank, so we use it for a full evaluation and analysis of different strategies before testing on the other languages. To understand the effect of target treebank size, we generate three datasets with different training sizes: T10 (∼10%), T50 (∼50%), and T100 (100%). We apply Morph and Nonce separately to understand the effect of each method and to control the amount of noise in the augmented data.