Cross-Lingual Dependency Parsing with Universal Dependencies and Predicted PoS Labels

This paper presents cross-lingual models for dependency parsing using the first release of the universal dependencies data set. We systematically compare annotation projection with monolingual baseline models and study the effect of predicted PoS labels in evaluation. Our results reveal the strong impact of tagging accuracy especially with models trained on noisy projected data sets. This paper quantifies the differences that can be observed when replacing gold standard labels and our results should influence application developers that rely on crosslingual models that are not tested in realis-

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