Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement
Table 5: F-score (and relative difference) of UDify and its refinement with RNG-Tr for a subset of dependency types on the concatenation of UD Treebanks.
['Type', 'UDify', 'UDify+RNG-Tr']
On average, we achieve 29.78% LAS error reduction compared to UDify by adding the RNG-Tr model to UDify. Significant improvements are achieved for all dependency types, especially in hard cases, such as copula (cop).