Efficient EUD Parsing
Table 2: Attachment scores for both UD trees and EUD graphs for the development treebanks using different dependency parsers: full baseline models (Full), distilled models (dist), and UDPipe v2.5 models (udpipe).
['[ITALIC]  [BOLD] Arabic', 'UAS', 'LAS', 'ELAS', '[ITALIC]  [BOLD] Bulgarian', 'UAS', 'LAS', 'ELAS']
We also evaluated the distilled models against the full baseline model and UDPipe v2.5. The distilled models outperform the UDPipe models and are within a point of both UAS and LAS to the full model. The ELAS results for the rule-based system using the predicted dependency trees from each of theses systems are also shown. The performance on ELAS generally follows the dependency scores. Our system is competitive mainly by the grace of our rule-based system which obtains an average 98.20 ELAS when used on the gold development treebanks. Having a rule-based system that can perform so well on gold data means that improving the dependency predictions it is based on for a full pipeline will almost always increase ELAS scores. It also means it could be used to generate new data. Although this would be restricted to generating data for pre-existing UD treebanks. Furthermore, it could be used to highlight annotation inconsistencies in a given treebank and between different treebanks for the same language.