Extracting Headless MWEs from Dependency Parse Trees:Parsing, Tagging, and Joint Modeling Approaches
Table A2: Dependency-parsing labeled attachment scores (LAS, %) on the test sets with bi-LSTM (top) and BERT (bottom) feature extractors. The cell containing the best result for each treebank has blue shading; results within one standard deviation of the best are in boldface.
['[ITALIC] w/ bi-LSTM Treebank', '[ITALIC] w/ bi-LSTM Treebank', 'Compl. Ratio ↓', 'Parsing', 'MTL Parsing', 'Joint Decoding']
We observe very little difference among the various strategies. This fact can be explained by the relatively low ratios of flat relations and the already-high base performance: the room for improvement on the standard LAS metrics is quite small.