Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement
Table 2: Labelled attachment scores on UD Treebanks for monolingual ([1] Kulmizev_2019 and DepBERT) and multilingual (UDPipe straka-2018-udpipe and UDify Kondratyuk_2019) baselines, and the refined models (+RNG-Tr).
['Language', 'Mono [1]', 'Multi UDPipe', 'Multi UDify', 'Multi+Mono UDify+RNG-Tr', 'Mono DepBERT', 'Mono DepBERT+RNG-Tr', 'Mono Empty+RNG-Tr']
We compare our models with previous state-of-the-art results (both trained mono-lingually and multi-lingually), based on labelled attachment score. The DepBERT model significantly outperforms previous state-of-the-art models on all UD Treebanks. But despite this good performance, the DepBERT+RNG- Tr model achieves further improvement over DepBERT in almost all languages and on average. As expected, we get more improvement when combining the RNG-Tr model with UDify, because UDify’s initial dependency graph contains more incorrect dependency relations for RNG-Tr to correct.