Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement
Table 1: Dependency parsing scores for different variations of RNG Transformer model on the development set of UD Turkish Treebank (IMST).
['Model', 'UAS', 'LAS']
We use both DepBERT and UDify as initial parsers. The DepBERT model significantly outperforms the UDify model, so adding the RNG-Tr model to the UDify model results in more relative error reduction in LAS compared to its integration with DepBERT (17.65% vs. 2.67%). In subsequent experiments, we use three refinement iterations with the stopping strategy, unless mentioned otherwise.