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We describe and evaluate different approaches to the conversion of goldstandard corpus data from Stanford Typed Dependencies (SD) and Penn-styleconstituent trees to the latest English Universal Dependencies representation(UD 2.2). Our results indicate that pure SD to UD conversion is highly accurateacross multiple genres, resulting in around 1.5% errors, but can be improvedfurther to fewer than 0.5% errors given access to annotations beyond the puresyntax tree, such as entity types and coreference resolution, which arenecessary for correct generation of several UD relations. We show thatconstituent-based conversion using CoreNLP (with automatic NER) performssubstantially worse in all genres, including when using gold constituent trees,primarily due to underspecification of phrasal grammatical functions.

All Roads Lead to UD: Converting Stanford and Penn Parses to English Universal Dependencies with Multilayer Annotations