Automatically Selecting the Best Dependency Annotation Design with Dynamic Oracles
Résumé
This work introduces a new strategy to compare the numerous
conventions that have been proposed over the years for
expressing dependency structures and discover the one for which a
parser will achieve the highest parsing performance. Instead of
associating each sentence in the training set with a single gold
reference, we propose to consider a set of references encoding
alternative syntactic representations. Training a parser with a
dynamic oracle will then automatically select among all alternatives
the reference that will be predicted with the highest accuracy.
Experiments on the UD corpora show the validity of this approach.
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