This paper presents several modifications of the standard annotation projection algorithm for syntactic structures in cross-lingual dependency parsing. Our approach avoids unnecessary dummy nodes and includes efficient data sub-set selection techniques that have a substantial impact on parser performance in terms of labeled attachment scores. We test our techniques on data from the Universal Dependency Treebank and demonstrate the improvements on a number of language pairs. We also look at treebank translation including syntax-base models and data combination techniques that push the performance even further. We achieve absolute improvements of up to almost six points in labeled attachment scores pushing the state-of-the art in cross-lingual dependency parsing for all language pairs tested in our experiments.
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