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We propose the Recursive Non-autoregressive Graph-to-Graph Transformerarchitecture (RNGTr) for the iterative refinement of arbitrary graphs throughthe recursive application of a non-autoregressive Graph-to-Graph Transformerand apply it to syntactic dependency parsing. We demonstrate the power andeffectiveness of RNGTr on several dependency corpora, using a refinement modelpre-trained with BERT. We also introduce Syntactic Transformer (SynTr), anon-recursive parser similar to our refinement model. RNGTr can improve theaccuracy of a variety of initial parsers on 13 languages from the UniversalDependencies Treebanks, English and Chinese Penn Treebanks, and the GermanCoNLL2009 corpus, even improving over the new state-of-the-art results achievedby SynTr, significantly improving the state-of-the-art for all corpora tested.

Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement