Parsing Natural Language Sentences by Semi-supervised Methods
Table 5: UAS of delexicalized parser transfer, averaged over 21 languages; some or all of the remaining 9 languages are used as source, according to the given subset name.
['Setup', 'High', 'Med', 'Low', 'Mix', 'All9']
It is easy to see that the conclusions presented in the previous section hold even for these datasets. Moreover, the differences in UAS are often much higher, especially for the smaller and highly diverse datasets High, Low and Mix, where the benefit of the Stanford style making the dependency trees more similar becomes quite important. This suggests that the role of Stanford style is stronger with small and heterogeneous datasets. For the High dataset, the best result surpasses the Prague-only baseline by as much as 2.24% UAS absolute.