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Dynamic-oracle Transition-based Parsing with Calibrated Probabilistic Output

We adapt the dynamic-oracle training method of Goldberg and Nivre (2012; 2013) to train classifiers that produce probabilistic output. Evaluation of an Arc-Eager parser on 6 languages shows that the AdaGrad-RDA based training procedure results in models that provide the same high level of accuracy as the averagedperceptron trained models, while being sparser and providing well-calibrated probabilistic output.