Rediscovering Annotation Projection for Cross-Lingual Parser Induction

Previous research on annotation projection for parser induction across languages showed only limited success and often required substantial language-specific post-processing to fix inconsis-tencies and to lift the performance onto a useful level. Model transfer was introduced as another quite successful alternative and much research has been devoted to this paradigm recently. In this paper, we revisit annotation projection and show that the previously reported results are mainly spoiled by the flaws of evaluation with incompatible annotation schemes. Lexicalized parsers created on projected data are especially harmed by such discrepancies. However, recently de-veloped cross-lingually harmonized annotation schemes remove this obstacle and restore the abilities of syntactic annotation projection. We demonstrate this by applying projection strate-gies to a number of European languages and a selection of human and machine-translated data. Our results outperform the simple direct transfer approach by a large margin and also pave the road to cross-lingual parsing without gold POS labels.